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Stage-dependent remodeling of projections to motor cortex in ALS mouse model revealed by a new variant retrograde-AAV9

  1. Barbara Commisso
  2. Lingjun Ding
  3. Karl Varadi
  4. Martin Gorges
  5. David Bayer
  6. Tobias M Boeckers
  7. Albert C Ludolph
  8. Jan Kassubek
  9. Oliver J Müller
  10. Francesco Roselli  Is a corresponding author
  1. University of Ulm, Germany
  2. University Hospital Heidelberg, Germany
  3. University of Kiel, Germany
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Cite this article as: eLife 2018;7:e36892 doi: 10.7554/eLife.36892

Abstract

Amyotrophic lateral sclerosis (ALS) is characterized by the progressive degeneration of motoneurons in the primary motor cortex (pMO) and in spinal cord. However, the pathogenic process involves multiple subnetworks in the brain and functional MRI studies demonstrate an increase in functional connectivity in areas connected to pMO despite the ongoing neurodegeneration. The extent and the structural basis of the motor subnetwork remodeling in experimentally tractable models remain unclear. We have developed a new retrograde AAV9 to quantitatively map the projections to pMO in the SOD1(G93A) ALS mouse model. We show an increase in the number of neurons projecting from somatosensory cortex to pMO at presymptomatic stages, followed by an increase in projections from thalamus, auditory cortex and contralateral MO (inputs from 20 other structures remains unchanged) as disease advances. The stage- and structure-dependent remodeling of projection to pMO in ALS may provide insights into the hyperconnectivity observed in ALS patients.

https://doi.org/10.7554/eLife.36892.001

Introduction

Amyotrophic lateral sclerosis (ALS) is classically described as a disease of upper and lower motor neurons (Ravits et al., 2007). However, postmortem studies have demonstrated that pathological hallmarks of ALS (such as TDP-43 inclusions) appear in motor cortex as well as in multiple cortical and subcortical structures during disease progression, depicting a propagation pattern (Brettschneider et al., 2013; Braak et al., 2013). Thus, ALS has been growingly conceptualized as a disease of the motor subnetwork or, in more advanced stages, a multi-network disease affecting motor, premotor and sensory areas. To which extent these networks are damaged by the neurodegeneration and remodel themselves during the disease progression, it is a subject of active investigation.

Diffusion-tensor imaging (DTI) studies have revealed that the degeneration of axons is detected in more and more white-matter tracts affecting different structures (corticospinal, corticorubral, corticostriatal and perforant tracts) at different stages of the propagation scheme (Neuroimaging Society in ALS (NiSALS) DTI Study Group et al., 2016; Kassubek et al., 2014; Kassubek et al., 2018) as well as in interhemispheric connections (Filippini et al., 2010) and in spinal cord (Cohen-Adad et al., 2013). However, analysis of resting-state networks by functional magnetic resonance imaging (fMRI) has revealed increased functional connectivity has been described in symptomatic ALS patients in the sensorimotor (Menke et al., 2016; Agosta et al., 2018) somatosensory (Agosta et al., 2011) and cortico-striatal networks (Fekete et al., 2013). Notably, an increase in functional connectivity in the motor subnetwork was already detectable in presymptomatic and early-stage ALS patients (Schulthess et al., 2016).

Although these data indicate that a significant architectural remodeling of the motor subnetwork takes place in ALS patients in the face of ongoing neurodegeneration, the extent of such remodeling and the structural changes underlying this effect in the motor network are unclear. Of particular interest is the extent of remodeling of the input network to pMO: in fact, the type and amount of projection to pMO determines not only the output from MO, which is strongly related to the execution of movements; in addition, the synaptic inputs are strong regulators of neuronal activity pattern and may influence activity-dependent pathogenic factors (Roselli and Caroni, 2015; Bading, 2017). To date, connectivity maps have been obtained at mesoscale (Oh et al., 2014; Mitra, 2014) or at cellular resolution (Mao et al., 2011; Wertz et al., 2015) only for wild-type models.

In order to address the remodeling of the subnetwork in an experimentally tractable condition, we have selected to quantitatively map the projections to the primary motor cortex network in the SOD1(G93A) ALS mouse model using a newly developed AAV variant endowed with retrograde tracing abilities. We have demonstrated that selected cortical areas display an increased projection to pMO already at presymptomatic stage and the increase in projection expands during disease progression to involve additional cortical and subcortical structures. We show that the pattern observed in the mouse model bears significant similarities with fMRI functional connectivity data gathered from human ALS patients. Thus, we have identified one structural component of the early and selective remodeling of the large-scale architecture of the motor subnetwork, which may contribute to explain the functional connectivity changes observed in patients.

Results

AAV9-SLR variant is a new, efficient viral tool for retrograde connectivity tracing

In order to identify a variant of AAV9 suitable for retrograde tracing, we considered three variants previously generated (AAV9-SLR, AAV9-NSS and AAV9-RGD) as part of an ongoing screening of the properties of newly generated AAV9 variants. These variants displayed a high infection efficiency in vitro in multiple cell lines (human coronary artery endothelial cells (HCAECs), human coronary artery smooth muscle cells (HCASMC), HEK293T, HeLa, 911, HepG2; Varadi et al., 2012), suggesting a potential for infecting neurons and neuronal processes with high efficiency; in particular, we evaluated them for highly desired applications such as high local infectivity and retrograde infectivity. We first tested infection rate of these AAV9 variants in vivo (Figure 1A). Five independent groups of tdTomato-ROSA26 reporter mice (N = 3 for each group, age P20) were injected in the dorsal striatum (DS) with 500 nl of viral suspension (9*1013 genomes/ml) of AAV9-SLR, AAV9-NSS, AAV9-RGD and, for comparison, WT-AAV9 and WT-AAV2 (Figure 1B). The total number of infected neurons in the injection area (local infectivity) and in regions projecting to DS was assessed at 15 days post-injection (DPI). One-way ANOVA revealed a significant difference (F(4,10)=198.8, p<0.0001) in the number of infected neurons per area unit (infection rate) in the injection ROI. Compared to WT-AAV9, infection rate was significantly higher for AAV9-SLR (post-hoc p=0.0001) but not for AAV-NSS, (p=0.0910), and was significantly lower for both AAV-RGD (p=0.0001) and WT-AAV2 (p=0.0001; detailed statistics are reported in Supplementary file 1a). In agreement with the limited toxicity of AAVs, no morphological sign of distress (axonal beading, dendrites fragmentation, fluorescent fragments of cell bodies) were identified in neurons infected with the AAV variants or with the WT serotypes.

AAV9-SLR is a new AAV variant with pronounced retrograde infectivity.

AAV9-SLR, AAV9-NSS and AAV9-RGD variants (or WT-AAV9 and WT-AAV2) were injected in dorsal striatum (N = 3) and local infection rate (B), retrograde infection to Substantia Nigra (C) and Motor Cortex (D) were assessed. (A) Experimental design showing injection site in dorsal striatum (DS) and brain regions that have been investigated for retrograde infection (SNr and MO). (B) AAV9-SLR displayed a significantly higher local infection rate than WT-AAV9 (143 ± 10%; post-hoc p=0.0001), whereas rAAV-NSS was comparable to WT-AAV9 (114 ± 8%, p=0.0910) and AAV-RGD was significantly less effective (8 ± 1%, p=0.0001). Detailed statistic provided in Supplementary file 1a. (B i-iv) representative images of injection site in DS. Scale bar 500 μm. (C): after DS injection, rAAV9-SLR produced a ten-fold increase in retrogradely infected TH +neurons compared to WT-AAV9 (1267 ± 152%, p=0.0001); rAAV9-NSS was more effective than WT-AAV9 (600 ± 100%, p=0.0001) but less effective than AAV9-SLR. WT-AAV2 and AAV-RGD did not retrogradely infect TH+ neurons. Detailed statistic provided in Supplementary file 1b. (C i-iv) representative images of SNr. Scale bar 30 μm. (D) after DS injection, rAAV9-SLR displayed the highest rate of retrograde infection in motor cortex (236 ± 32% of WT-AAV9, p=0.0001), followed by rAAV9-NSS (145 ± 25% p=0.0909), both more effective than WT-AAV9. Detailed statistic provided in Supplementary file 1c. (D i-iv) representative images of motor cortex. Scale bar 100 μm. (E) Experimental design showing injection site in primary visual cortex (V1) and brain regions that have been investigated for retrograde infection (LGN). After injection in primary visual cortex injection (V1, N = 3), retrograde infection of neurons in lateral geniculate nucleus (LGN) was assessed. (F) rAAV9-SLR displayed a strong retrograde infection of LGN neurons (700 ± 240%, p=0.0005), followed by AAV9-NSS (400 ± 133% of WT-AAV9, p=0.0446). No LGN infection resulted from the injection of WT-AAV2 or AAV9-RGD. Detailed statistic provided in Supplementary file 1d. (F i-iv) Representative images of LGN. Scale bar 30 μm. Manual cell counting was normalized on the volume of the injection sites, values are expressed in terms of percentage over AAV9-WT. Ordinary one-way ANOVA with Dunnett multiple comparison test was used.

https://doi.org/10.7554/eLife.36892.002

When screened for their ability to retrogradely infect neurons projecting to the injection site (in DS), the AAV variants differed significantly from WT-AAV9 in terms of retrogradely infected neurons located in substantia nigra (SNc; one-way ANOVA F(4,10)=135.1, p>0.0001; detailed statistics are reported in Supplementary File 1b) and motor cortex (MO; F(4,10)=46.5, p<0.0001; Supplementary File 1c). Injection of AAV9-SLR and AAV9-NSS resulted in a larger number of tdTomato+/tyrosine-hydroxylase-positive (TH+) neurons in SNc than WT-AAV9 (AAV9-SLR: p=0.0001; AAV9-NSS: p=0.0001; Figure 1C), whereas injection of WT-AAV2 and AAV-RGD resulted in no retrograde infection to SNc. Increased retrograde infection from DS to MO (Figure 1D) was detected for AAV9-SLR (p=0.0001) but not for AAV9-NSS (p=0.0909), whereas WT-AAV2 (p=0.1153) and AAV9-RGD (p=0.0018) had a significantly lower retrograde infection rate. Finally, we evaluated the retrograde infection rate from visual cortex (V1) to lateral geniculate nucleus (LGN, Figure 1E). Injection of 500 nl of suspension of each of the five AAV variants in V1 of reporter tdTomato/ROSA26 (N = 3) resulted in a significantly different number of tdTomato + neurons in LGN (one-way ANOVA, F(4,10)= 17.8p=0.0002; Figure 1F). AAV9-SLR and AAV9-NSS infected a significantly larger number (up to 10-fold) of LGN neurons than WT-AAV9 (AAV9-SLR p=0.0005; AAV9-NSS: p=0.0446; detailed statistics are reported in Supplementary file 1d), whereas, neither WT-AAV2 (p=0.7941) nor AAV9-RGD (p=0.7271) resulted in any infection in the LGN. Taken together, these data identified AAV9-SLR as a new AAV variant endowed with robust, reproducible and broad retrograde infection capability.

Mapping input to primary motor cortex by AAV9-SLR

We then exploited the AAV9-SLR for mapping the forebrain and subcortical neurons projecting to primary (pMO, Figure 2A) or secondary (sMO) motor cortex in WT mice. Retrogradely labeled neurons in the cortical and subcortical structures of the forebrain were quantified, anatomically annotated and normalized for the volume of the injection site (Figure 2B,C). Overall, we identified 28 distinct anatomical structures providing direct input to the injection site in pMO, including thalamic nuclei (TH; Figure 2D), followed by homolateral somatosensory cortex (SS; Figure 2D), contralateral secondary motor cortex (cMOs) and contralateral primary motor cortex (cMOp; Figure 2D). Values for raw neuronal counts (before normalization) are displayed in Figure 2—figure supplement 1A, detailed statistic provided in Supplementary file 1e. Detailed statistic after normalization for injection site volume are in Supplementary file 1f. When projecting neurons are expressed in terms of percentage of the total population of projecting neurons (Figure 2—figure supplement 1B), TH represented 32 ± 7% of the total input to pMO, SS 22 ± 7%, cMOs 8 ± 3% and cMOp 6 ± 2% (full details are provided for all structures in Supplementary file 1g). Additional projections were provided by auditory cortex (AUD 6 ± 1%; Figure 2D) and contralateral somatosensory cortex (cSS 5 ± 5%) and, among subcortical structures, by caudoputamen (CP 6 ± 2%), claustrum (CLA 3 ± 1%) and basolateral amygdala (BLA 0.4 ± 0.3%). We consistently identified a small contingent of neurons projecting to pMO located in hypothalamus (HY 0.6 ± 0.2% of the total pool of neurons projecting to pMO in the forebrain); out of these hypothalamic neurons, not previously reported, the majority was located in the lateral zone of hypothalamus (zona incerta 23 ± 10% of the total of hypothalamic neurons projecting to pMO; lateral preoptic area 23 ± 19% of the total of hypothalamic neurons projecting to pMO; lateral hypothalamic area 14 ± 18% of the total of hypothalamic neurons projecting to pMO). In addition to previously reported input structures to pMO (Hooks et al., 2013; Oh et al., 2014; Mao et al., 2011), we identified additional inputs from paraventricular hypothalamic area (PAL 0.8 ± 0.2) and gustatory area (GU 0.7 ± 0.3%).

Figure 2 with 2 supplements see all
Projections to primary and secondary motor cortex in P20 wild-type.

(A) Experimental design depicting injection site in pMO and relative projecting neurons. (B) List of the regions projecting to primary MO (pMO) identified by injection of AAV9-SLR in pMO of WT mice (representative injection site in panel (C) scale bar 300 µm). Total neuronal count has been normalized for the volume of the injection site (raw neuronal counts in Supplementary file 1e; detailed statistic on normalized counts provided in Supplementary file 1f;). The largest contribution to the input to pMO was provided by thalamus (TH), followed by somatosensory (SS), contralateral secondary motor cortex (cMOs) and contralateral primary motor cortex (cMOp). Additional inputs came from: auditory cortex (AUD), caudoputamen (CP), contralateral SS (cSS), CLA, baso-lateral amygdala (BLA) and hypothalamus (HY). List of the regions projecting to primary motor cortex in terms of total neuronal count and in term of percentage over the total pool of positive neurons in Figure 2—figure supplement 1A–B, detailed statistic provided in Supplementary file 1e and 1g. (D) Representative images of retrogradely-labelled neurons projecting to pMO. (i) Ipsilateral brain section displaying main regions targeting primary motor cortex. Scale bar 500 μm. (ii) Detail of SS, scale bar 200 μm. (iii) Detail of AUD, scale bar 200 μm. (iv) Detail of cMOp, scale bar 200 μm. (v) Detail of TH, scale bar 200 μm. (E) Experimental design depicting injection site in sMO and relative projecting neurons. (F): List of the regions projecting to secondary MO (sMO) identified by injection of AAV9-SLR in sMO of WT mice (representative injection site in panel (G) scale bar 300 µm). Total neuronal count has been normalized for the volume of the injection site (detailed statistic provided in Supplementary file 1e-1g). Input toward pMO and sMO is qualitatively similar (display of total neuronal count in Figure 2—figure supplement 1C, detailed statistic provided in Supplementary file 1e-1g). List of the regions projecting to secondary motor cortex in terms of percentage over the total pool of positive neurons in Figure 2—figure supplement 1D, detailed statistic provided in Supplementary file 1f. Projections from thalamus, to both pMO and sMO, have been further broken down for each thalamic nucleus (Figure 2—figure supplement 2A–D, detailed statistic provided in Supplementary file 1h-1i-1j). Abbreviations: Thalamus (TH), homolateral somatosensory cortex (SS), contralateral secondary motor cortex (cMOs), contralateral primary motor cortex (cMOp), auditory cortex (AUD), caudoputamen (CP), contralateral somatosensory cortex (cSS), claustrum (CLA), contralateral caudoputamen (cCP), temporary association area (TEa), ectorinal cortex (ECT), contralateral anterior cingulate (cAC), contralateral claustrum (cCLA), agranular insular area (AI), orbital cortex (ORB), contralateral orbital cortex (cORB), paraventricular hypothalamic area (PAL), perirhinal area (PERI), basolateral amygdala (BLA), hypothalamus (HY), gustatory area (GU), visceral area (VISC), entorhinal area (ENT), contralateral anterior part of anterior commissure (cACA), endopiriform nucleus (EP), piriform area (PIR), contralateral gustatory area (cGU), contralateral piriform area (cPIR).

https://doi.org/10.7554/eLife.36892.004

The input to the neighboring sMO was qualitatively similar to pMO, including all of the structures projecting to pMO (all in all, 20 distinct regions were found to project to sMO compared to 28 for pMO). Values for raw neuronal counts (before normalization) are displayed in Figure 2—figure supplement 1C, detailed statistic provided in Supplementary file 1e). The trend remained unchanged after normalization for injection site volume (Figure 2E–G; detailed statistic Supplementary file 1f). When projecting neurons are expressed in terms of percentage of the total population of projecting neurons (Supplementary file 1, Figure 2D), SS emerged as contributing a significantly larger share of input to sMO than to pMO (33 ± 3%, p<0.0001 vs pMO), followed by structures whose input to pMO and sMO was comparable (detailed values reported in Supplementary file 1g): TH (28 ± 1%, p=0.5965), AUD (10 ± 2%, p=0.1914), cMOs (7 ± 3%, p>0.9999), cMOp (4 ± 1%, p=0.9815), orbital area (ORB: 2 ± 0.4%, p>0.9999) and BLA (2 ± 0.7%, p>0.9999).

Since the thalamus appeared to provide a significant fraction of the projections to pMO and sMO, we annotated the labelled neurons to thalamic nuclei (raw and normalized neuronal counts are reported in Supplementary file 1h and 1i, respectively). In total, 19 thalamic nuclei were found to project to either pMO or sMO motor cortex (Figure 2—figure supplement 2A–F). When considered as a fraction of the total number of thalamic neurons projecting to pMO or sMO, the ventral anterior-lateral complex and the ventral medial nucleus appeared to be the most relevant source of projection to MO, and their contribution to pMO or sMO was comparable (VAL to pMO 29 ± 6%, to sMO 34 ± 6%, two-way ANOVA p=0.6496) and ventral medial nucleus (VM to pMO 24 ± 7%, to sMO 25 ± 12%, p>0.9999; detailed values reported in Supplementary file 1j).

Increased input to pMO in early presymptomatic mSOD

We then exploited the retrograde AAV9-SLR to quantitatively explore the projections to pMO in mSOD mice and WT littermates during disease progression. First, we focused on early-presymptomatic stages: AAV9-SLR was injected in pMO at P20 and animals were sacrificed at P35 (Figure 3A). Retrogradely labeled neurons in the cortical and subcortical structures of the forebrain were quantified, anatomically annotated and the raw counts normalized according to the volume of the injection site (Figure 3B,C). Strikingly, we found an overall increase in the number of neurons projecting to pMO in mSOD compared to WT both in raw counts (Supplementary file 2a and Figure 3—figure supplement 1A) and after normalization (Two-way ANOVA, brain regions F(26,189)=53.0 p<0.0001, genotype F(1,189)=16.3 p<0.0001). Post-hoc analysis (Figure 3B; detailed numerical values in Supplementary file 2b) revealed a statistically significant increase in the mean number of neurons projecting to pMO from SS (effect size: 1.6; p<0.0001) and from cMOs (effect size: 1.6; p=0.0189). Notably, among the 28 anatomical regions considered, all others displayed a comparable number of neurons projecting to pMO in WT and mSOD (p>0.05 WT vs mSOD for all the brain regions). For all regions considered, the median value of neurons projecting to pMO was close to the mean value, and the skewness value never exceeded 1.7 (with 16/28 regions in WT and 28/28 regions in mSOD having values ranging between −1.0 and +1.0), discounting the possibility of strongly biased or non-normal distributions.

Figure 3 with 1 supplement see all
Increased projections to primary motor cortex in mSOD at pre-symptomatic stage (P20) from ipsilateral SS and contralateral sMO.

(A) Experimental design depicting injection site in pMO and relative projecting neurons. (B) Number of neurons, normalized for injection site volume, projecting to primary motor cortex in WT (N = 6) and mSOD (N = 3). Significant increase in the number of neurons projecting to pMO from SS (WT vs mSOD, p<0.0001) and from cMOs (WT vs mSOD, p=0.0189). No other statistical significant differences between WT and mSOD among the 28 identified structures. Detailed statistic provided in Supplementary file 2b. Input to pMO expressed in terms of total neuronal count in Figure 3—figure supplement 1A, detailed statistic provided in Supplementary file 2a. (C) Representative images of neurons labeled by retrograde tracing from SS, in WT and mSOD, respectively. Scale bar 150 µm. (D) Cortical layer allocation of neurons projecting to primary motor cortex from SS, cMOp, cMOs and AUD out of AAV9-SLR injection. Despite the overall increase in projection from SS to pMO in mSOD mice, a significant loss of projections from layer V of SS was detected (WT 46 ± 9% vs mSOD 30 ± 2%, p=0.0324). Detailed statistic provided in Supplementary file 2c. Values are expressed in term of percentage over the total number of neurons for each structure. Bar chart displaying cortical projections in terms of total neuron in Figure 3—figure supplement 1B, detailed statistic is provided in Supplementary file 2c and 2d. Two-way ANOVA with Sidak correction for multiple comparison.

https://doi.org/10.7554/eLife.36892.010

We further investigated the pattern of neurons projecting to pMO from structures with increased projection (SS, cMOs) and unaltered projection (cMOp, AUD) annotating them by cortical layers (detailed values before and after normalization for injection site volume and statistical analysis reported in Supplementary file 2c and d). Interestingly, despite the overall increase in the number of neurons projecting to pMO, a significant loss of projecting neurons was detected in SS layer V of mSOD mice (effect size: −1.5; p=0.0324, Supplementary file 2a) mirrored by a reciprocal trend toward increase projection from layer II/III (effect size: 1.2; p=0.2734) but not in layer VI (p=0.9898). (Figure 3D; values expressed in terms of percentage of the total population of projecting neurons, detailed statistic Supplementary file 2e). On the other hand, the distribution of projecting neurons across cortical layers was comparable in WT and mSOD for cMOs, cMOp and AUD (data before normalization displayed in Figure 3—figure supplement 1B).

To rule out any infectivity bias of the virus in WT vs mSOD mice, we verified that the selective increase in projection from SS to pMO could be detected by an independent method. We injected pMO with fluorescently labeled cholera toxin B (CTb) and assessed the number of CTb-labeled neurons in SS and cMOp (showing increased or unchanged projection to pMO, respectively, in the viral tracing experiment); neuronal counts were normalized for injection volume (Figure 4A–C; counts before normalization are depicted in Figure 4—figure supplement 1A, and numerical values in Supplementary file 2f). Confirming the viral tracing data, CTb labeled a larger number of neurons in the SS in the mSOD compared to WT (both N = 3; one-way ANOVA F(3,8)=17.0, p=0.0008), whereas no difference was detected in cMOp between the two genotypes (SS effect size: 1.4; p=0.0156, cMOp effect size: −0.4; p=0.9971; Figure 4A–C and Supplementary file 2g).

Figure 4 with 1 supplement see all
Increased projections to primary motor cortex in mSOD at pre-symptomatic stage (P20) is specific for motor network and is confirmed via the retrograde tracer choleratoxin.

(A) Experimental design depicting injection site in pMO via choleratoxin (CTb) with two regions that have been investigated for retrograde infection (SS and cMOp). (B) Normalized (for injection site volume) number of neurons projecting to primary motor cortex, WT (N = 3) vs mSOD (N = 3), via Choleratoxin-b injection. CTb + retrogradely labeled projections from SS and cMOp to pMO. Increased projections from SS to pMO was again detected in mSOD (WT 1235 ± 280; mSOD 2487 ± 819, p=0.0156); projections from cMOp were comparable (WT 308 ± 67, mSOD 283 ± 59, p=0.9971), as in the virus tracing experiment. Detailed statistic provided in Supplementary file 2g. Display of total neuronal count in Figure 4—figure supplement 1A, detailed statistic for total neuronal count in Supplementary file 2f. (C) Representative images of SS from WT and mSOD, respectively, displaying CTb + neurons, scale bar 200 μm. (D) Experimental design depicting injection site in in primary visual cortex (V1) via AAV9-SLR with two regions that have been investigated for retrograde infection (LGN and MO). (E) Normalized (for injection site volume) number of neurons projecting to visual network (V1) from LGN (WT 42 ± 18 vs mSOD 60 ± 38; p=0.9263) and MO (WT 134 ± 77 vs SOD 176 ± 74, p=0.6360) in WT (N = 3) and mSOD (N = 3). No difference in projections from the two areas to V1 was found between WT vs mSOD. Detailed statistic provided in Supplementary file 2i. Display of total neuronal count in Figure 4—figure supplement 1B, detailed statistic in Supplementary file 2h. (F) Representative images of MO projection neurons to V1 in WT vs mSOD; scale bar 150 μm. Ordinary one-way ANOVA with Dunnett multiple comparison test.

https://doi.org/10.7554/eLife.36892.014

We further sought to rule out any differential infectivity of the virus in mSOD model by quantifying the neurons projecting to primary visual cortex (V1) from LGN and MO in P20 mice (Figure 4D–F). Injection of AAV9-SLR in V1 resulted in a comparable number of labelled neurons (either before either after normalization for the injection site volume: Supplementary file 2h and 2i) in WT (N = 3) and mSOD (N = 3) both in LGN (p=0.9263) and in MO (p=0.6360). Counts before normalization are depicted in Figure 4—figure supplement 1B.

In contrast to our observation in the pMO, input to sMO was comparable in WT (N = 3) and mSOD (N = 4) mice (either before either after normalization; for normalized two- way ANOVA, genotype F(1,100)=0.7572 p=0.3863. Figure 5A–C and Supplementary file 2j and 2k for non-normalized and normalized counts, respectively). After normalization, a trend toward increased projection to sMO in mSOD was detected only in cMOs (effect size: 1.3; p=0.0708). In conclusion, retrograde tracing of the input to pMO revealed an unexpected increase in the number of neurons projecting from SS and cMOs to pMO as early as P20. This reveals that remodeling of cortical connectivity is already initiated in presymptomatic mSOD mice.

No change in projections to secondary motor cortex, at pre-symptomatic stage (P20).

(A) Number of neurons, normalized for injection site volume, projecting to secondary motor cortex (sMO) in WT (N = 3) and mSOD (N = 3). No statistically significant differences in input to sMO were identified between mSOD and WT mice, although a trend toward increased projection in mSOD was found in contralateral sMO and AUD (two-way ANOVA with Sidak correction for multiple comparison). Detailed statistic in Supplementary file 2k. Total neuronal count has been reported in Supplementary file 2j. (B i-ii-iii) Representative brain sections displaying main regions targeting secondary motor cortex, scale bar 500 μm. (C) Representative images of neurons labeled by retrograde tracing from SS (i–ii), cAUD (iii-iv) and BLA (v–vi). Scale bar 150 μm. Analysis for total neuronal count is provided in Supplementary file 2h.

https://doi.org/10.7554/eLife.36892.017

Cortical and subcortical structures display an increased input to pMO in mSOD during disease progression

We then investigated how the projections to pMO changed during disease progression. We compared the number and anatomical locations of neurons projecting to pMO in mSOD and WT mice at the early-symptomatic age, P60 (WT N = 3, mSOD N = 4) and symptomatic age, P90 (WT N = 5, mSOD N = 5; Figure 6A, B); investigation of later timepoints was prevented by the observation, in a preliminary study, of high intraoperative mortality (3 out of 3 mSOD mice, possibly due to respiratory failure) in mice injected at P115. The number of neurons projecting to pMO from the different structures was expressed as fold-change of the age-match WT (detailed values reported in Supplementary file 3a). Distribution of the projections to pMO was stable over time in WT mice (two-way ANOVA; brain regions F(19,209)=0.001 p>0.99). However, when compared to their age-matched WT littermates at P60, mSOD mice displayed an increase in neurons projecting to pMO (two-way ANOVA; brain regions F(19,100)=2.3 p=0.0034) from SS (effect size: 1.6; p=0.0019; unchanged compared to P20 mSOD, p>0.9999) and cMOs (effect size: 1.6; p=0.0454; unchanged compared to P20 mSOD, p>0.9999). In addition, now an increased number of neurons projecting to pMO was found in cMOp (effect size: 1.4; p=0.0077 in mSOD vs WT littermates,+2.4 ± 0.9 vs P20 mSOD, p=0.0110). Besides these three, no other structure, among the 28 analyzed, showed a significant difference between WT and mSOD (Figure 5A–B and Supplementary file 3a). Investigation of the symptomatic stage (P90) revealed a further, significant modification of the abnormal pattern of projections to pMO (two-way ANOVA; brain regions F(19, 160)=7.6, p<0.0001), affecting both cortical and subcortical regions. Projections to pMO from SS was still increased (effect size:1.6; p=0.0095; same increase registered for P20 and P60 mSOD, p>0.9999 for both), cMOp (effect size: 1.7; p<0.0001, also increased when compared to the P60 timepoint:+1.6 ± 0.5 vs P60 mSOD, p=0.0124) and cMOs (Effect size: 1.4; p<0.0001, comparable to the increase registered in mSOD at P20 (p=0.7658) and P60 (p=0.3917)). Moreover, increased number of projecting neurons was detected from TH (effect size: 1.7; p=0.0014;+1.8 ± 0.4 vs P20 mSOD, p=0.0064), AUD (effect size: 1.6; p<0.0001;+1.4 ± 0.5 vs P20 mSOD, p<0.0001), and CP (effect size: 1.7; p<0.0001;+2.0 ± 0.5 vs P20 mSOD, p=0.0086). As for the P20 timepoint, median values were close to the mean value of relative change in the number of projecting neurons and skewness ranged between −1.0 and +1.0, indicating the absence of biased distributions of the data.

Figure 6 with 1 supplement see all
Progressive changes in projections to pMO in disease progression.

(A) Projection neurons were mapped at P20, P60 and P90 timepoints; fold change of normalized total number of projecting neurons of mSOD vs WT was plotted for the three stages of disease progression. Values were expressed in terms of fold change over the corresponding WT for each time point. At P60 (WT N = 3, mSOD N = 4), increased connectivity persisted from SS (2.3 ± 0.5 in mSOD vs WT littermates, p=0.0108) and cMOs (2.0 ± 0.3 in mSOD vs WT littermates, p=0.0381), but, in addition, cMOp showed an increased projection to pMO (2.2 ± 0.8, p=0.0323 in mSOD vs WT littermates). At P90 (WT N = 5, mSOD N = 5), further significant increase in projection to pMO was found in: TH (2.4 ± 0.6 in mSOD vs WT littermates, p=0.0002), SS (2.2 ± 0.4 in mSOD vs WT littermates), cMOp (3.4 ± 1.0 in mSOD vs WT littermates, p<0.0001), cMOs (2.8 ± 1.3 in mSOD vs WT littermates, p<0.0001), AUD (4.0 ± 1.6 in mSOD vs WT littermates, p<0.0001) and CP (2.9 ± 0.7 in mSOD vs WT littermates, p<0.0001). (B) Representative images, for P90 stage, of neurons labeled by retrograde tracing from TH (i–ii), cMOp (iii-iv) and HY (v–vi). Scale bar 150 μm. Two-way ANOVA with Sidak correction for multiple comparison. Detailed statistic provided in Supplementary file 3a. Analysis of number of neurons in pMO for later stage (P90) mSOD animals provided in Figure 6—figure supplement 1A–B.

https://doi.org/10.7554/eLife.36892.019

To exclude an effect of a widespread neuronal loss in the pMO of mSOD on the number of axons projecting to pMO, we performed a NeuN staining in the pMO of P90 mSOD mice (n = 3) revealing no difference in the total number of neurons in our injection area (p=0.4987, Figure 6—figure supplement 1A).

Taken together, these findings revealed a stage- and structure-dependent remodeling of input to pMO, characterized by the progressive increase in the number of neurons projecting to the pMO, with cortical areas affected at earlier stages and subcortical areas only at later stages.

Pyramidal layer V neurons across multiple areas of the motor subnetwork display simultaneous loss of spines on basal dendrites

Having demonstrated an increase in projection to pMO from specific cortical and subcortical structures with a progression-dependent pattern, we sought to determine if this phenotype could be correlated with pathogenic processes ongoing in the projecting neurons themselves. In order to verify if the neurons projecting to pMO already displayed signs of ALS-related pathogenic process, we elected to use dendritic spine density as readout. In fact, dendritic spine loss has been reported to be one of the earliest morphological signs of pathological involvement of several cortical and subcortical neuronal subpopulations in ALS (Ozdinler et al., 2011; Fogarty et al., 2016; Fogarty et al., 2017). Pyramidal neurons located in layers II/III and V were acquired from: SS (whose projection to pMO was increased in early stage of the disease), cMOp (increased projection starting from intermediate stage of the disease) and AUD (increased projection only at the later stages). We also included a sample of layer V and layer II/III neurons from ipsilateral pMO (known to be affected by the disease already at early stages; Fogarty et al., 2017).

At early pre-symptomatic stage (when increased projections from SS, but not from AUD, are already detectable), overall spine density was comparable in neurons in SS, cMO and AUD projecting to pMO from WT and mSOD mice (one-way ANOVA, F(13,108)=0.8, p=0.6317; Figure 7A and Supplementary file 3b). Interestingly, layer-V neurons projecting to the pMO in SS, AUD and cMO displayed a decrease in spine density from P60 (Figure 7B), (F(13,119)=7.8, p<0.0001); at the same time- point, a decrease in dendritic spine density in neurons from layer V of pMO itself became evident (p=0.0071; in agreement with what previously reported; Ozdinler et al., 2011; detailed numerical values are reported in Supplementary file 3c). Unlike layer V, we found no statistically significant change in the spine density in layer II/III at P60 in any of the cortical areas considered. At P90, a significant decrease in spine density affected neurons projecting to pMO from all structures under scrutiny either in layer II/III either in layer V (effect size between −1.6 and −1.8; F(13,128)=47.8, p<0.0001, Figure 7C and Supplementary file 3d).

Pyramidal neurons projecting to pMO display simultaneous spines loss on basal dendrites during disease progression independently of misfolded SOD accumulation.

(A) Spine density on pyramidal neurons projecting to primary at P20; each dot representing average spine density for one single neuron. Spine density was comparable in pyramidal neurons projecting to pMO in WT and mSOD mice. Detailed statistic provided in Supplementary file 3b. (B) Neurons projecting to primary motor cortex started to display spine loss at P60. Pyramidal neurons in layer V belonging to cMOp (4 ± 1 spines/10 μm in WT vs 2 ± 1 spines/10 μm in mSOD; p=0.0030), SS (4 ± 1 spines/10 μm in WT vs 3 ± 1 spines/10 μm in mSOD; p=0.0047) and AUD (5 ± 1 spines/10 μm in WT vs 3 ± 1 spines/10 μm in mSOD; p=0.0034) of mSOD mice displayed a significant decrease in spine density compared to their WT counterpart animals. Projecting neurons in layer II/III displayed a spine density comparable to WT. Detailed statistic provided in Supplementary file 3c. (C) Significant decrease in spine density affected the whole cortex when the latest stage was reached, at age of P90: cMOp layer II/III (WT 6 ± 1 spines/10 μm in WT vs 2 ± 1 spines/10 μm in mSOD, p=0.0001), layer V (4 ± 1 spines/10 μm in WT vs 2 ± 0.5 spines/10 μm in mSOD, p<0.0001); SS layer II/III (6 ± 1 spines/10 μm in WT vs 3 ± 1 spines/10 μm in mSOD, p<0.0001), layer V (4 ± 0.6 spines/10 μm in WT vs 1 ± 0.6 spines/10 μm in mSOD, p<0.0001); AUD layer II/III (5 ± 1 spines/10 μm in WT vs 2 ± 0.6 spines/10 μm in mSOD, p<0.0001), layer V (4 ± 1 spines/10 μm in WT vs 1 ± 0.5 spines/10 μm in mSOD, p<0.0001). Detailed statistic in Supplementary file 3d. (D i-ii) comparison of basal dendrite stretched between WT and mSOD at P20 in contralateral pMO layer V, scale bar 5 μm. D iii-iv: comparison of basal dendrite stretched between WT and mSOD at P90 in contralateral pMO layer V. Scale bar 5 μm. One-way ANOVA with Sidak correction.

https://doi.org/10.7554/eLife.36892.023

In conclusion, the loss of spines did not temporally correlate with the increased-projections phenotype, suggesting that the latter may not be interpreted as a consequence of pathology in projecting neurons (within the limits of the spine-loss readout). In addition, we show that spine loss affects not only vulnerable neurons in pMO, but rather neurons across the whole motor subnetwork.

misfSOD expression is not related to network remodeling nor to spine pathology

Next, we investigated if the build-up of misfolded SOD1 (misfSOD1) in neurons projecting to pMO may be related to the increased-projection phenotype. MisfSOD1 has been reported to be a hallmark of mSOD-ALS pathobiochemistry (Bosco et al., 2010) and has been identified as the earliest disease marker in vulnerable spinal MN (Saxena et al., 2013). To this aim, mice were injected with fluorescently labeled CTb in pMO at P20 and sacrificed at P35, in order to label the neuronal subpopulations projecting to pMO; misfSOD1 was revealed by the B8H10 monoclonal antibody against one conformational epitope of misfolded SOD (Pickles et al., 2013). In pMO, where upper MN are mainly (but not exclusively) localized, already at this age 41 ± 7% of neurons in pMO layer V were positive for misfSOD +compared to only 1 ± 0.5% of layer II/III neurons. MisfSOD1 burden (assessed by the immunofluorescence intenty) in misfSOD1 +neurons was comparable in pMO layer II/III (1798±355) and layer V (2189 ± 393; p>0.05). We then identified the neurons in SS and AUD projecting to pMO by CTb retrograde labelling (the non-viral approach was used to avoid possible artifacts due to the over-expression of ZSGreen) and assessed the mSOD burden by immunostaining (Figure 8A–C). Both SS and AUD included a misfSOD +subset of neurons in layer II/III (20 ± 5%) and layer V (54 ± 6%). However, when we considered the CTb + and CTb- subpopulations of neurons, we found that, in layers II/III and V the population of neurons projecting to pMO (CTb+) displayed a significantly lower burden of misfSOD1 than the overall neuronal population of the CTb- neurons (effect size between −0.9 and −1.4; one-way ANOVA, F(11,1242) = 35.3, p<0.0001; Figure 8A–C and detailed numerical values in Supplementary file 3e) in both cortical layers of SS (p<0.0001) as well as in both cortical layers of AUD (p<0.0001). In order to verify that this finding was not due to a confounding effect of CTb itself, we traced a different subpopulation of SS neurons, namely those involved in inter-hemispheric projections; we injected fluorescently labeled CTb in contralateral SS of mSOD1 mice at P30 and we assessed the burden of misf SOD in CTb + and CTb- neurons (Figure 8D; E). In contrast to what observed in the subpopulation connected to pMO, SS subpopulation projecting to contralateral SS displayed a range of misfSOD levels comparable to non-projecting, nearby neurons (p=0.2696) including, in both cases, neurons with high burden and neurons with low burden of mSOD (Figure 8D–E and Supplementary file 3f). Thus, taken together, these findings suggest that although within regions part of the motor subnetwork there is a mixed population of neurons with high and low burden of misfSOD, the neurons directly projecting to pMO constitute a subpopulation with comparatively low misfSOD burden.

misfSOD expression does not trigger network remodeling nor loss of spines in basal dendrites of connected pyramidal neurons.

(A) Experimental design depicting injection site in pMO via choleratoxin (CTb) with two regions that have been investigated for misfSOD immunostaining intensity (SS and AUD). (B) Comparison of misfSOD expression between neurons CTb + and CTb- in layer II/III and layer V of SS and AUD in mSOD. In both areas, a subset of neurons in layer II/III and layer V displayed misfSOD accumulation at P20. Compared to the overall population of neurons in layer II/III and V (CTb-), the populations of neurons projecting to pMO (CTb+) displayed a lower burden of misfSOD: SS layer II/III CTb + 1179 ± 350, CTb- 1730±418, p<0.0001; SS layer V CTb + 1073 ± 238, CTb- 1706±445, p<0.0001; AUD II/III CTb + 1318 ± 325, CTb- 1755±518, p<0.0001; AUD layer V CTb + 888 ± 164, CTb- 1372 ± 329, p<0.0001. One-way ANOVA with Sidak correction, detailed statistic reported in Supplementary file 3e. (C i-ii) Magnification of SS and AUD layer V respectively, red stains misfSOD antibody, green stains for CTb+, scale bar 50 μm. (D) Experimental design depicting injection site in SS via CTb with contralateral SS investigated for misfSOD immunostaining intensity. (E) comparison of misfSOD expression between neurons CTb + and CTb- in layer V of contralateral SS in mSOD. Injection of CTb was performed in SS. The populations of neurons projecting to SS display the same burden of misfSOD: CTb + 1770 ± 400, CTb- 1782±442, (t Test p=0.2696). Detailed statistic is reported in Supplementary file 3f. (F) Spine density analysis on two differential neuronal populations, misfSOD- and misfSOD+, projecting from SS to primary motor cortex at P60; each dot representing average spine density for one single neuron. No difference has been detected (one-way ANOVA with Sidak correction, SS layer II/III p>0.9999, SS layer V p>0.9999), detailed statistic reported in Supplementary file 3g. (G) Representative images of misfSOD – and misfSOD+ neuron in SS layer V. i-ii-iii: misfSOD+ neuron displaying ZsGreen (green) and misfSOD (red, scale bar 20 µm) together with a stretch of its basal dendrite (scale bar 5 µm). iv-v-vi: misfSOD- neuron displaying ZsGreen (green) and misfSOD (red, scale bar 20 µm) together with a stretch of its basal dendrite (scale bar 5 µm).

https://doi.org/10.7554/eLife.36892.025

Next, we asked if the spine loss phenotype observed in neurons projecting to pMO at later timepoints could be correlated with misfSOD build-up. To this aim, P60 mSOD/ZsGreen mice were injected with rAA9-SLR in pMO (N = 3) and the burden of misfSOD was assessed in SS ZsGreen + neurons (layer II/III and layer V) by immunostaining with the B8H10 monoclonal antibody (Figure 8F; G). Based on misfSOD intensity thresholds (the 10th and 90th percentile values of the single-neurons distribution shown in Figure 8B), we discriminated two population of ZsGreen + neurons (high and low misfSOD expression, either in layer II/III either in layer V) for which the spine density on basal dendrites was counted. Interestingly, despite the extreme difference in misfSOD burden, spine density was comparable in two groups and was within the range of WT mice for layer II/III neurons and reduced (compared to WT) for layer V neurons (in agreement with the values in Figure 7B; statistic in Supplementary file 3g), suggesting that spine loss may be a non-cell-autonomous phenomenon not directly related to the burden of mistSOD.

Pattern of functional connectivity alterations in ALS patients displays similarity to the changes in projections in ALS mouse model

Having identified a subgroup of cortical areas and subcortical structures whose projection to pMO was increased in mSOD mice, we set out to verify if a similar set of areas display increased connectivity to pMO in ALs patients. To this aim, we analyzed the connectivity pattern in a large cohort of 71 ALS patients compared to 28 healthy controls by using ‘resting-state’ fMRI. The connectivity patterns were studied using a connectome-based approach in order to investigate region-to-region functional connectivity in a manner allowing comparison of results between the patient and mSOD mouse studies. Each of the a priori defined seed regions allowed for the identification of well-known functional brain networks in healthy humans (Smith et al., 2009) (Figure 9A), which indicated robust seed locations for the functional connectome analysis. Overall functional connectivity analysis demonstrated no statistically significant differences between groups (t = −0.51, p=0.613), which rules out a potential bias in the overall level of connectivity (van den Heuvel et al., 2017). However, network-based statistics of functional connectome data revealed significantly increased functional connectivity (hyper-connectivity) between regions frontal to the pMO, namely the dorsolateral prefrontal association cortex and the pMO itself, both homolaterally and contralaterally (t ≥ 2.81, p≤0.0065, corrected; Figure 9B). Functional connectivity between left and right primary somatosensory cortex was decreased (t = 2.93, p=0.0048) and no effect was detected between thalamic nuclei and pMO or in visual cortex. Thus, the fMRI data from ALS patients confirmed the occurrence of a large-scale remodeling in the motor subnetwork (as observed in the murine dataset and confirming previously published data; Schulthess et al., 2016) and showed a predominant increase in connectivity between pMO and both homolateral and contralateral frontal-lobe areas, showing similarity with the increase projection from contralateral rostral structures, such as sMO, observed in the mouse model.

Functional connectivity alterations in human ALS.

(A) BOLD synchronization illustrated as coronal (upper panel) and axial connectivity (lower) heat maps showing voxel-wise Fisher’s r-to-z transformed correlation coefficients (thresholded for |z(r)|≥0.4) for which the fMRI BOLD signal was correlated with the respective seed-voxel forming the motor system (upper panel) and the visual association system (lower panel) in healthy elderly human subjects (N = 28). (B) Pairwise region-to-region functional connectivity strength analysis between schematically illustrated regions (blue circles) corresponding to seed voxels (as shown in A) revealed significantly altered functional motor system connectivity (red arrows) in ALS patients (N = 71) compared to matched healthy controls (N = 28). PMC = Primary motor cortex, PSS = Primary somatosensory cortex, DLPAC = Dorsolateral prefrontal association cortex, V2/3 = Visual association area V2/V3, Th = thalamus.

https://doi.org/10.7554/eLife.36892.027

Discussion

In the present work, we have demonstrated, using viral-tracing approaches, that the motor subnetwork in an ALS mouse model undergoes distinct remodeling during disease progression with stage and structure-dependent effects: projection from SS is increased already in presymptomatic mice and remains stable, whereas projections from AUD and cMOs and cMOp either appear or increase further in the symptomatic stage. We show that this pattern displays similarities with the network remodeling in the form of increase functional connectivity changes observed by fMRI in ALS patients. Thus, we provide cellular-resolution data that contribute to analysis of large-scale architecture of motor circuits in ALS mouse model, which may provide entry points to understand changes occurring in human patients.

In order to map the input to the MO in mSOD and WT mice, a retrograde viral-tracing strategy was considered. We have introduced a new variant of AAV9 which displays significantly higher capacity for retrograde infection of neurons in vivo. Although retrograde tracing can be achieved with non-viral approaches, these methods suffer from limited sensitivity, stability and do not provide genetic access to the target neurons (Tervo et al., 2016). Retrograde tracing has been previously achieved in vivo using rabies-derived vectors (Wickersham et al., 2007), herpes simplex virus (Ugolini et al., 1987) or canine adenovirus (Soudais et al., 2001). These vectors, however, have been plagued by the intrinsic toxicity of the expressed viral proteins (as in the case of rabies - Ginger et al., 2013 - although new rabies variants may have overcome this issue, see Ciabatti et al., 2017) or by the tendency to activate inflammation (Muruve, 2004). Recently, the development of retrograde AAV vectors have been undertaken to allow retrograde tracing with genetic access and low toxicity; in particular, a retrograde AAV2 variant has been reported (rAAV2; Tervo et al., 2016). Our AAV9 variant extends and complements the existing toolset of AAV vectors endowed with retrograde infectivity. Although rAAV2 belongs to the AAV2 serotype and may strongly bind heparan sulphate, our AAV9-derived retrograde AAV variant has the advantage of reduced binding to heparin sulfate (Shen et al., 2011) and therefore it is less affected by changes in brain extracellular matrix (e.g. due to neuroinflammation associated with neurodegeneration); AAV9 is only infrequently targeted by pre-existing immunity to AAVs (Zhang et al., 2011; Saraiva et al., 2016) and may provide the entry point to enhanced vectors for circuit-specific gene therapy in humans since AAV9 has been already successfully tested for this application (Boutin et al., 2010).

We first validated the tool by mapping the input connectivity to pMO and sMO at cellular resolution; notably, the discreet pattern of regions containing retrogradely labelled neurons separated by others containing or no labeled cell, in a reproducible pattern and matching the list of brain structures projecting to pMO (e.g. Hooks et al., 2013) and the Allen Brain Connectivity atlas) suggest that the infection of distant neurons is not an artifact of passive viral diffusion or infection via blood supply (which would have resulted in a continuous or erratic distribution of labelled neurons). We further validated the reliability of our approach in the context of the mSOD1 mouse, verifying that viral-tracing results on SS and pMO could be reproduced by non-viral tracing (CTb) and that no abnormalities could be found in the projections to visual cortex, an area spared the pathogenic process. Nevertheless, AAV vectors relie on retrograde transport to reach the cell body (where expression of the genome takes place; Castle et al., 2014b) in process dependent on dynein/dynactin (Castle et al., 2014a) and one may speculate that the transport slow-down and dysfunction reported in ALS (Bilsland et al., 2010; Marinkovic et al., 2012) may bias the quantitative evaluation of the tracing experiments. However, the slowing of the transport would result in the decrease or delayed arrival of the AAV to the neuronal cell body, actually decreasing the chances of Cre expression and possibly resulting in decreased number of projecting neurons; therefore, this mechanism is unlikely to underlie the observed increase in the number of projecting neurons.

We exploited this new retrograde AAV9 to investigate in ALS models the qualitative and quantitative changes in the structure of the motor subnetwork, in particular of the projections to the pMO. A major finding of the present work is the increase in the numbers of neurons projecting from several cortical and subcortical regions to pMO; the increase is selective for some cortical areas only and the number of regions involved increases over time. The increase in the number of neurons projecting to a fixed, small volume of pMO is most likely to be interpreted as due to the expansion of the axonal arborizations of these neurons in pMO as disease progresses and it is in agreement with the increased frequency of EPSPs observed early in disease course (Fogarty et al., 2015). What may be the driving force of this expansion? One may hypothesize that a pathogenic process ongoing in projecting neurons may drive the expansion; however, neither the burden of misfolded SOD1 nor the signs of structural disturbances (i.e. the decrease in spine density) were detectable in the areas displaying increased projection to pMO. On the other hand, it has been demonstrated in several models (e.g. in the neuromuscular unit), that silencing of either the pre- or post-synaptic site is sufficient to induce axonal sprouting (Caroni et al., 1994; Caroni, 1997). Since reduced firing of vulnerable spinal MN has been shown to precede degeneration (Martínez-Silva et al., 2018; Roselli and Caroni, 2015), it is tempting to speculate that the expansion of the projections may be driven by abnormal activity of vulnerable post-synaptic neurons in the pMO (including vulnerable corticospinal MN; Ozdinler et al., 2011). Indeed, in mSOD mice excitability changes appear to affect a larger set of neurons in pMO than previously thought (Kim et al., 2017).

When the progressive increase in projections to pMO detected in the tracing experiments are compared with the changes in connectivity reported here and by others (Agosta et al., 2018; Menke et al., 2016; Schulthess et al., 2016), several similarities emerge: (i) both in human and mouse data, we observe a progressive remodeling of the motor subnetwork, (ii) the remodeling of the motor subnetwork is detectable before the appearance of overt clinical or behavioral abnormalities, (iii) the remodeling of the motor subnetwork involves the primary motor cortex and more rostral, homolateral or contralateral structures (in the frontal lobe, prefrontal cortex or secondary motor cortex in human and mouse, respectively) and projecting to pMO, and (iv) both in human and mouse data the primary somatosensory cortex appears involved in the motor subnetwork remodeling (either in terms of increased projection to pMO in the mouse, or in terms of connectivity between left and right SS, SS, sensory-motor or SS to other cortical areas; Douaud et al., 2011; Agosta et al., 2011). Nevertheless, thalamocortical connectivity does not appear to be altered in the functional connectivity studies.

Based on these similarities, might the increase in projection to pMO be a leading mechanism underlying the functional hyperconnectivity in ALS patients? Although the relationship between structural and functional connectivity is not precise, severals studies have demonstrated that, at large-scale level, structural connectivity is a strong predictor of functional connectivity (Honey et al., 2009; Honey et al., 2007; Damoiseaux and Greicius, 2009; Hermundstad et al., 2013) and maps of functional and structural connectivity display high degrees of similarity (van den Heuvel et al., 2009). Moreover, studies in non-human primates comparing functional connectivity with anatomical tracing have revealed that anatomical connectivity strongly (although not exclusively) shape the functional connectivity (Shen et al., 2012; Shen et al., 2015). On the other hand, deducing structural connectivity from functional connectivity data can be fraught with inaccuracies, since indirect connections may contribute to it (Adachi et al., 2013). Thus, on the assumption that the observations made on the remodelling of the motor subnetwork in mouse models could be extrapolated to humans, the expansion of projections to pMO may contribute a structural substrate of the functional hyperconnectivity observed in fMRI studies. Notably, alternative models have been proposed to explain the hyperconnectivity phenotype. Changes in functional connectivity could also result from an alteration in the local excitation/inhibition balance due to the dysfunction of inhibitory interneurons (Douaud et al., 2011): in fact, pathological studies have found that calbindin-positive GABAergic interneurons undergo degeneration in ALS (Maekawa et al., 2004), lending support to the hypothesis that loss of inhibitory tone contributes to clinical dysfunction in ALS (Turner and Kiernan, 2012). The two models are actually not mutually exclusive: the pattern of either increased either decreased connectivity in resting-state (Jelsone-Swain et al., 2010; Mohammadi et al., 2009; Zhou et al., 2013; Agosta et al., 2013; Douaud et al., 2011) and task-based fMRI (Heimrath et al., 2014; Konrad et al., 2002; Lulé et al., 2009) may be the result of ongoing degeneration and loss of axons (Dadon-Nachum et al., 2011; Sarica et al., 2014) accompanied by potentially compensative phenomenon (Bernier et al., 2017). The observed increase in projection from cortical and subcortical areas might be an early adaptive response to ongoing cell dysfunction up to the point where a critical cell loss is reached and a disconnection syndrome begins to emerge (Gorges et al., 2017).

In addition to the study of large-scale patterns of projections to vulnerable cortical areas, the use of retrograde viral vectors has allowed the access to morphological features of a distinct population of neurons selected by connectivity rules (i.e. all neurons projecting to pMO). Our analysis has revealed a stage-dependent loss of spines in all neurons projecting to pMO (first in layer V, then in layer II/III), which appears independent of misfSOD burden and involves neuronal populations in areas whose vulnerability was previously unrecognized, such as AUD neurons. Spine loss has been detected as a morphological counterpart of the early stages of the degenerative process ongoing in the layer V of the motor cortex of mSOD mice (Fogarty et al., 2015). The longitudinal study of multiple brain areas in Golgi-staining preparations (Fogarty et al., 2016) has revealed the early and progressive spine loss affecting the basal dendrites of pyramidal neurons from MO, medial pre-frontal (MPFC) cortex; notably, SS neurons appeared to be affected only very late in disease progression (P120; Fogarty et al., 2016). By identifying a neuronal subpopulation of SS based on connectivity rules (i.e. only the neurons projecting to pMO), we report an earlier loss of spines than previously though (P50 vs P120). This finding not only underscores the importance of assessing disease manifestations in homogeneous (and characterized by connectivity rules) populations but also suggests that spine loss may be a phenomenon occurring in a larger set of neurons, possibly across the whole motor subnetwork.

Our study displays some limitations. Firstly, in order to maintain tractability while still addressing the core question of the large-scale remodeling of the structure of projections in the motor subnetwork, we decided to limit the analysis to the forebrain, avoiding the analysis of brainstem nuclei and cerebellum. Although these regions provide relevant input to the motor subsystem, they are not usually identified in fMRI studies of ALS and therefore their contribution to the definition of the hyperconnectivity phenotype may be more limited. Second, although it is desirable to have large number of biological replicates, the need to maintain the genotype blinding and to manually identify and anatomically annotate each neuron (more than 150,000 for the present work) did not allow for a n > 3–6, thus underpowering the study for the comparison of structures with fewer number of projecting neurons. Although the present study is in line with the numerosity currently manageable in manual reconstructions (Hooks et al., 2013; Yamawaki et al., 2016; DeNardo et al., 2015; Tervo et al., 2016; Kohl et al., 2018), the advancement of machine learning and automated neuronal annotation (Fürth et al., 2018) may allow a more comprehensive quantitative analysis of input network to pMO.

Third, we did not consider ipsilateral sMO and pMO for cell counting in our analysis. Due to the proximity of pMO and sMO, we could not have conclusively excluded the possibility that infected neurons in the ipsilateral sMO were derived from retrograde infection or virus diffusion, introducing ambiguity to the connectivity map. Further refinement of the retrograde-tracing approach is ongoing to provide a whole brain, full resolution map of the connectivity of the motor subsystem in ALS.

Taken together, our circuit-tracing data in mice and imaging data in humans converge to show that a significant and progressive remodeling of cortical circuits takes places in ALS and that this phenotype may be amenable to investigation in animal models. The mechanistic drivers of this remodeling are currently unclear, although it is possible to speculate that homeostatic mechanisms may be activated by alterations in excitability of pMO neurons, including corticospinal motor neurons (Kim et al., 2017). In fact, pathological decrease or increase in the firing properties of vulnerable neuronal subpopulations appears to be a shared phenotype of multiple neurodegenerative conditions (Roselli and Caroni, 2015) and may be one of the driving forces of adaptive cortical remodeling in neurodegeneration (LANDSCAPE Consortium et al., 2015; Rosskopf et al., 2017; Rosskopf et al., 2018). Thus, the combination of the analysis of large-scale projection patterns (enabled by retrograde viral tracing) with chronic manipulation of excitability and firing (such as chemiogenetics; Saxena et al., 2013) may prove fundamental to link functional and structural cortical phenotypes.

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource of referenceIdentifiers/RRIDSAdditional information
AntibodyAnti-Red
Fluorescece
Protein (RFP)
Rockland600-401-379/
RRID: AB_828390
(1:1000)
AntibodyDAPIInvitrogenD1306(1:1000)
AntibodyAnti-Tyrosin-
Hydroxilase
SigmaT2928/
RRID: AB_477569
(1:4000)
AntibodyAnti-misfolded
SOD1 (B8H10)
MediMabsMM-0070/
RRID:AB_10015296
(1:1000)
AntibodyDonkey anti
rabbit Alexa-568
Life TechnologiesA10042(1:500)
AntibodyAnti NeuNMilliporeMAB377/
RRID:AB_2298772
(1:100)
AntibodyDonkey
anti-mouse
Alexa-488
Life TechnologiesA21202(1:500)
Strain, strain
background
(Mus musculus)
B6SJL-Tg(SOD1
*G93A)1Gur/J
Jackson LaboratoriesRRID:IMSR_JAX:002726high-copy, henceforth mSOD
Strain, strain
background
(Mus musculus)
B6.Cg-Gt(ROSA)
26Sortm6/
(CAG-ZsGreen)Hze/J
Jackson Laboratories007906henceforth ZsGreen-ROSA26
Strain, strain
background
(Mus musculus)
B6.Cg-Gt(ROSA)
26Sortm6/(CAG-Td
Tomato)Hze/J
Jackson Laboratories007914henceforth tdTomato-ROSA26
Chemical
compound,
drug
Phenol
Red Solution
Sigma‐AldrichP0290
Chemical
compound,
drug
Protease
inhibitor mix
Serva39101.03
Chemical
compound,
drug
Polyethylenimine (PEI)Polysciences23966
Chemical
compound,
drug
OptiprepProgen1114542
Chemical
compound,
drug
BuprenorphineReckitt Benckiser
Chemical
compound,
drug
MeloxicamBöhringer Ingelheim
Chemical
compound,
drug
Ketamine 10%WDT
Chemical
compound,
drug
Rompun 2%
(Xylazin)
Bayer
Recombinant
DNA reagent
AAV9-RGDLRVS
-CMV-Cre
Varadi et al., 2012
Recombinant
DNA reagent
AAV9-SLRSPPS
-CMV-Cre
Varadi et al., 2012
Recombinant
DNA reagent
AAV9-NSSRFTP
-CMV-Cre
Varadi et al., 2012
Recombinant
DNA reagent
WT-AAV2-
CMV-Cre
Werfel et al., 2014
Recombinant
DNA reagent
WT-AAV9-CMV-CreWerfel et al., 2014
Peptide,
recombinant
protein
Alexa-488-conjugated
cholera toxin B
InvitrogenC34775
Peptide,
recombinant
protein
Alexa-647-conjugated
cholera toxin B
InvitrogenC34778

Animals

All animal experiments in this study were performed in agreement with the guidelines for the welfare of experimental animals issued by the Federal Government of Germany and by the local ethics committee (Ulm University) and authorized under licence no. 1312. The following strains of transgenic mice were obtained from Jackson Laboratories: B6SJL73 Tg(SOD1*G93A)1Gur/J (high-copy, henceforth mSOD), B6.Cg-Gt(ROSA)26Sortm6/(CAG74 ZsGreen)Hze/J (henceforth ZsGreen-ROSA26) and B6.Cg-Gt(ROSA)26Sortm6/(CAG75 TdTomato)Hze/J (henceforth tdTomato-ROSA26). For the generation of double-transgenic lines, heterozygous male mice from the mSOD line were crossed with females homozygous for the reporter cassette (either ZsGreen or tdTomato lines). Resulting progeny was 50% WT/ROSA26ZsGreen+ and 50% mSOD/ROSA26ZsGreen+. Under the repression of the STOP cassette in the ROSA26 locus, the fluorescent protein (either ZsGreen either tdTomato) is not expressed in basal conditions in the reporter lines; however, upon expression of the Cre recombinase (in our experimental design, via an AAV vector) leads to the excision of the STOP cassette (since it is flanked by the lox recombination sites) and results in the expression of the reporter (Gerfen et al., 2013).

Male mice were used for experimental purposes at post-natal day (P)20, P60 and P90 (sacrificed at P35, P75 and P105). Mice were housed at 3–4 animals per cage, with unlimited access to food and water, a light/dark cycle of 14/10 hr and humidity between 40% and 60%. All mice expressing mSOD were routinely tested for motor impairment and euthanized in case of overt motor disability. All male mice from a given litter were employed and the experimenter was kept blind to their genotype: injection, perfusion, brain section, imaging and neuronal counts and anatomical annotation; the genotype was revealed only when each mouse was assigned to a group for the statistical analysis.

All efforts were made to comply to the ‘3R’ guidelines. In order to maintain the data analysis tractable and in absence of previous data on quantitative analysis of input connectivity in ALS mice, we estimated the sample size based on the mean number and standard deviation of thalamic neurons at P90, anticipating a genotype effect of at least 30–40%, with α = 0.05% and 90% power, resulting in group size between 3 and 5.

Generation and production of AAVs

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AAV-vectors were generated using a three plasmid standard protocol (Jungmann et al., 2017) with a pscAAV-CMV-Cre vector genome plasmid, derived from pscAAV-CMVEnh/MLC0.26-Cre (Werfel et al., 2014), pDGdeltaVP providing helper sequences, and a distinct rep-cap-helper plasmid with either AAV2 or AAV9 wild-type capsid genomes (resulting in WT-AAV2-CMV-Cre or WT-AAV9-CMV-Cre, respectively), or AAV9 variants containing distinct heptapeptide motifs reported previously (Varadi et al., 2012), resulting in AAV9-RGDLRVS-CMV-Cre, AAV9-SLRSPPS-CMV-Cre and AAV9-NSSRFTP-CMV-Cre. Vectors were purified and concentrated as described before (Jungmann et al., 2017).

Intracerebral injections

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Intracerebral injection of AAV vectors was performed as previously reported (Karunakaran et al., 2016). Briefly, mice were administered buprenorphine (0.01 mg/Kg; Reckitt Beckiser Healthcare, Berkshire, UK) and meloxicam (1.0 mg/Kg; Böhringer Ingelheim, Biberach an der Riß, Germany) 30 min before being put into a stereotaxic frame (Bilaney Consultants GmbH D-40211 Düsseldorf, Germany) under continuous isoflurane anesthesia (4% in O2 at 800 ml/min). Skin scalp was incised to expose the underlying bone and a hand-held micro drill was used to drill the burr hole in the opportune location. Stereotactic coordinates were chosen for each target area based on The Mouse Brain in Stereotaxic Coordinates (as in Oh et al., 2014) and corresponded to primary motor cortex (x =+ 1.5, y =+ 1.0, z = −0.5), secondary motor cortex (x =+ 0.7, y =+ 1.0, z=-0–5), striatum (x =+ 1.0, y =+ 1.5, z = −3.5), visual cortex (x = −3.0, y =+ 2.0, z = −0.6) and primary somatosensory cortex(x =+ −1.0, y = −1.0, z = 0.5). 500 nl of viral suspension, mixed with an equal volume of 1% Fast Green solution (100 mg of Fast Green in 10 mL of H2O), was injected using a pulled-glass capillary connected to a Picospritzer microfluidic device. The Fast-Green visible dye, used to visually monitor the injection, is quickly washed away in PBS and does not interfere with the imaging procedures. Injection was performed with 10 msec pulses every 30 s over a span of 5 min. The capillary was kept in place for 10 more minutes before being withdrawn to prevent backflow of the virus. Scalp skin was then stitched with Prolene 7.0 surgical thread and the animals were transferred to single cages with facilitated access to water and food for recovery. Animals were monitored for eventual neurological impairment for the following 72 hr and were administered additional doses of buprenorphine as needed.

Each of the following AAV vectors was independently injected at the titre of 5–9*1013/ml: wild-type (WT) AAV9-CMV-Cre (WT-AAV9), WT-AAV2-CMV-Cre (WT-AAV2), AAV9-RGDLRVS-CMV-Cre (AAV9-RGD), AAV9-SLRSPPS-CMV-Cre (AAV9-SLR) and AAV9-NSSRFTP-CMV-Cre (AAV9-NSS).

For cholera toxin injections, Alexa-488-conjugated or Alexa-647-conjugated cholera toxin B (CTb-488, Invitrogen) was diluted according to the manufacturer’s instructions to 1 µg/µl by gentle mixing (no vortexing). 1.0 µl of CTb was injected using the same procedure as reported above.

Immunohistochemistry

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Mice were terminally anesthetized with cloralium hydrate and transcardially perfused with 50 ml ice-cold PBS followed by 2.5–3 ml/g of 4% paraformaldehyde (PFA) in phosphate buffered saline (PBS, pH 7.4). Brain samples were quickly dissected and post-fixed in 4% PFA in PBS at 4°C for 18 hr, washed in PBS and cryo-protected in 30% sucrose in PBS for 36 hr. Samples were then snap-frozen in OCT and sectioned at −15°C in a cryotome (Leica CM1950) to 70µm-thick sections serially collected. Free-floating brain sections were incubated for 2 hr at 24°C in blocking buffer (5%-donkey serum, 3% BSA, 0.3% Triton X-100), followed by incubation with primary antibodies, such as: rabbit anti-RFP (1:1000, Rockland Immunochemicals, Limerick, PA) for 24 hr at 4°C; mouse anti-Tyrosine Hydroxylase (1:2000, Sigma) for 24 hr at 4°C; mouse anti NeuN (1:100, Millipore) for 24 hr at 4°C; mouse monoclonal anti-misfolded SOD1 (B8H10, 1:1000, MediMabs) for 48 hr at 4°C. Brain sections were thereafter washed 3 × 45 min in PBS, 0.1% Triton, incubated for 2 hr at 24°C with the opportune secondary antibody combination in blocking buffer (donkey anti-rabbit Alexa-568; donkey anti-mouse Alexa-488, 1:500; Life Technologies), together with DAPI (0.1 µg/ml, Life Technologies), and washed again 3 × 45 min in PBS, 0.1% Triton. Brain sections were mounted using ProLong Gold Antifade (ThermoFisher) mounting medium.

Image acquisition and analysis

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In order to analyze the local infection rate of the virus in the dorsal striatum (DS), we identified the core of injection according to the brightness level. Four regions of interest (ROI, 300 × 300 µm2 each) were drawn at its cardinal points and 500 µm3 of tissue was analyzed in total. For each ROI, reporter +neurons and DAPI +nuclei were counted. Local infectivity for each AAV variant was calculated by averaging ROI values for every mouse (N = 3 for each viral variant) and the values are expressed as a percentage of AAV9 variants over WT- AAV9. For analysis of the retrograde infectivity in motor cortex (MO), substantia nigra (SNc), thalamus (TH) and lateral geniculate nucleus (LGN), brain slices corresponding to our target brain regions were cut (N = 3 for each viral variant). Reporter +soma were counted for every region and the values are expressed in terms of percentage over WT-AAV9.

For analysis of input connectivity to primary motor cortex (pMO, considering all time points analyzed: WT N = 11, SOD N = 12) and visual cortex (VIS, WT N = 3, SOD N = 3) via AAV9-SLR variant, all sections corresponding to each brain were scanned using the BZ-9000 fluorescence microscope (KEYENCE) equipped with a 4x objective, with exposure time set at 200 msec. All images were acquired in a 12bit format. Images were processed using the ImageJ software suite: images of individual brain sections were loaded, background was subtracted offline via the appropriate tool to unambiguously identify reporter +cells. Labeled cells were positively identified as neurons (in contrast to non-neuronal cells or to dense clusters of axons) by the detection of a round soma surrounded by basal dendrites and, in cortical neurons, by the presence of an apical dendritic shaft. The soma of fluorescently labeled neurons was manually identified and annotated for anatomical brain regions according to Allen Brain Atlas coordinates. Total numbers of neurons were normalized (Oh et al., 2014) by the volume of the injection site in pMO (AAV9- SLR: 1.3 ± 0.6 mm3, N = 23) and VIS (AAV9-SLR: 1.1 ± 0.5 mm3, N = 6). Cortical layers were discriminated according to positional criteria and using the tissue autofluorescence (which highlights nuclei in negative, since they are not strongly autofluorescent) and the region of interest (ROI, rectangular in shape, approximately 200 µm high) was placed approximately 100 µm (upper border) ventral to the cortical (pial) surface for layer II/III, whereas for layer V and VI the lower border of the ROI was placed 300 µm dorsal of, or in contact with, respectively, the boundary between gray and white matter. The correctness of this positions was checked in a subset of NeuN and DAPI-stained sections.

For analysis projecting neurons we injected a small volume (500 nl out of a suspension of a titre of 5–9*1013/ml of virus suspension) of AAV9-SLR virus suspension in the pMO corresponding to the whisker area (according to Zingg et al., 2014) or in the neighboring secondary motor cortex of tdTomato-ROSA26 or ZsGreen-ROSA26 reporter mice (age P20). After sacrificing the animals 15 days post-injection, brains were serially sectioned to 70 µm sections, and each section was digitized for manual annotation. An average of 5000 + neurons were manually identified (N = 6). In order to make the analysis tractable, cerebellum and brainstem nuclei were not included in the present study. Moreover, although ipsilateral sMO is a well-known input to pMO, its analysis was not performed because of technical issues: its proximity to the injection site made it difficult to distinguish between retrograde infection and spreading of the virus.

For analysis of input connectivity to pMO via cholera toxin (CTb Alexa Fluor 488 conjugate, Thermofisher, WT N = 3, SOD N = 3), brain sections were imaged with a Zeiss 710, 20x objective (NA 0.8), 0.9 optical zoom at 12 bit depth. CTb + soma were manually counted in contralateral pMO (cMOp) and somatosensory cortex (SS) following anatomical coordinates according to the Allen Brain Atlas and neuronal counting was normalized to the injection site volume (1.2 ± 07 mm3, N = 6).

For analysis of neuronal cells number in pMO, 70 µm brain slices were cut for WT (N = 2) and SOD (N = 2) mice at the age of P90. Slices were stained for NeuN (Millipore). Images were acquired with AF6000 fluorescence microscope (Leica) equipped with a 10x objective, with exposure time set at 200 msec. All images were acquired in a 12-bit format. Single optical section images of pMO were analyzed for NeuN +soma via Imaris 7.6.5 software (Bitplane AG, ‘Spot’ tool, followed by manual correction).

For spine density analysis, fluorescent images were acquired with a Zeiss 710 confocal microscope, with a 63x oil-immersion objective (NA 1.40) and 0.9 optical zoom at 12bit depth. Cortical neurons expressing the reporter protein, therefore part of the motor network, were acquired in their full dimension, that is dendrites did not exit the plane of the coronal brain slice before reaching the dendritic terminus. Criteria for selection of neurons were anatomical and morphological: selected neurons were located in the desired cortical layer, showing the distinctive pyramidal shape and displaying at least three uncorrupted basal dendrites. For each neuron, three to six, 30–100 μm long, intermediate dendritic segments (10 µm distant from the tip) were acquired for spine counts. After image collection, each segment was analyzed for spine density using the Imaris 7.6.5 software (Bitplane AG, ‘filament tracer’ tool followed by manual correction). Only protrusions with a clear connection of the head of the spine to the dendrite shaft were counted as spines; small processes were classified as a spine only if they were <3 µm long and <0.8 µm in cross- sectional diameter (Harris, 1999). Spine density was then expressed as the number of spines per 10 μm dendrite length (Saba et al., 2016; Jara et al., 2012; Vinsant et al., 2013). Number of mice N = 3 for both WT and SOD for each time point.

To analyze misfSOD1 expression distribution within the motor network, we performed pMO injection of mSOD animals at P20 (sacrificed at P35, N = 3) with CTb. Brain slices were stained with the mouse monoclonal anti misfolded SOD1 B8H10 antibody (MediMabs, Montreal, Canada). Imaging was performed with a Zeiss 710, 20x objective (NA 0.8), 0.9 optical zoom at 12bit depth and images were analyzed using ImageJ. For misfSOD1 +neurons assessment, an ROI of 165 mm2 was drawn for each cortical layer, a threshold was set and only neurons above the threshold were considered positive. For counting CTb + neurons in layer II/III, we selected a total range of 200 µm, 100 µm away from the Pia and for layer V, we considered a total range of 200 µm, 400 µm away from the grey matter. The perimeter of misfSOD +nuclei was manually drawn for both CTb + and CTb- in order to calculate and compare their mean grey value.

Statistical analysis - mouse experiments

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Statistical analysis was performed with the GraphPad Prism six software suite. For comparing normalized neuronal counts between viral variants, ordinary one-way ANOVA with Dunnett multiple comparison test was used. The same test was applied for analysis of input to V1 using AAV-SLR and to pMO using CTb. For pMO and sMO input connectivity via AAV9-SLR, including comparison between the different cortical layers, either raw and normalized neuronal counts was compared by two-way ANOVA with Sidak correction for multiple comparison. Data regarding NeuN +neuronal count in primary motor cortex and misfSOD expression in CTb-labeled neurons have been analyzed via unpaired t-Test with Mann-Whitney correction. For the characterization of the variability of the dataset and for the description of the distribution of the data points, coefficient of variation (standard deviation/mean) and Skewness coefficient have been calculated (and are provided in tabulated format along with the mean, median and standard deviation).

Skewness value provides a descriptor of the slope of data distribution. In a perfect normal distribution curve, data distribute symmetrically on both sides of the peak, and the value of the skewness is zero. In a non-gaussian distribution, skewness value is positive when data piles up on the peak’s left side and negative when data piles up on the peak’s right side (and the tail points left). Skewness value between −2 and +2 are accepted as indication of non-biased data distribution (George and Mallery, 2010).

The magnitude of the difference between WT and mSOD was described using the parameter known as the effect size ((mean mSOD-mean WT)/SD of either group; calculated according to Sullivan and Feinn, 2012). Unlike significance tests, effect size is independent of sample size (Ferguson, 2009).

Data are displayed as mean ± SD and values for median, skewness and effect size are provided in tables as supplementary file. Statistical significance was set at p<0.05 before multiple-comparisons correction: statistical significance is indicated by p<0.05 and the magnitude of the effect is highlighted by the effect size. Percentage changes are reported in relation to relative WT.

Functional connectivity data validation in humans

Ethical approval of human study

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All subjects included in the human study provided written informed consent according to institutional guidelines; the consent includes the declaration of the understanding of the study design, the agreement to the participation to the study, to the publication of the results, and to the data protection and anonymization procedures (under the chaptes Einwilligungserklaerung’, ‘Probandeninformation’, ‘Darstellung der Experimente’, ‘Datenschutzerklärung’). The study was approved by the Ethics Committee of Ulm University, Ulm, Germany (reference #19/12) and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Data acquisition in humans

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‘Resting-state’ functional magnetic resonance imaging (rs-fMRI) data were acquired from 71 symptomatic patients with ALS, diagnosed according to the recently revised El Escorial diagnostic criteria (for The WFN Research Group On ALS/MND et al., 2015), and 28 matched healthy controls (see Table 1 for clinical and demographic characteristics). Human whole-brain based echo planar images (EPI) were acquired for all subjects at a 3T Siemens Allegra MR scanner (Siemens Medical, Erlangen, Germany) using a blood oxygen level dependent (BOLD) sensitized rs-fMRI sequence (30 transversal slices, gap 1 mm, 3 × 3×4 mm3 voxels, 64 × 64×30 matrix, FOV 192 × 192×149 mm3, TE 30 ms, TR2000ms, flip angle 90°, 300 volumes).

Table 1
Subject demographics and clinical characterization.
https://doi.org/10.7554/eLife.36892.028
Healthy controlsALS, all, T0p-value
Subjects (number)2871NA
Gender (male:female)15:1339:321.000*
Age (years)54.8 (±12.9), 22.4—75.758.4 (±13.7), 19.7—85.10.228
Duration of disease (month)NA19.2 (±17.6), 2.6—84.7NA
Age of onset (years)NA56.8 (±13.9), 19.2—84.6NA
ALSFRS-RNA40 (±5), 24—48NA
Rate of disease progression§ (1/month)NA0.8 (±1.2), 0.0—7.8NA
  1. Data are shown as mean (±std), min—max. All values were computed using the MATLAB (The Mathworks Inc, Natick, MA) based ‘Statistics Toolbox’.

    *Fisher’s exact test refers to comparison between all ALS patients and healthy controls.

  2. †Two-sample unpaired t-test assuming unequal variances refers to comparison between all ALS patients and healthy controls.

    ‡ALSFRS-R, revised ALS Functional Rating Scale (maximum score 48, falling with increasing physical impairment).

  3. §Rate of disease progression computed as (48 - ALSFRS-R)/(disease duration)(Menke et al., 2014). NA, not applicable.

Preprocessing of functional data

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The human functional data analysis followed a standardized procedure (Gorges et al., 2014) and was performed using the Tensor Imaging and Fiber Tracking (TIFT) software package (Müller et al., 2007). Preprocessing included (1) spatial upsampling to an 1 × 1×1 mm3 isogrid (matrix, 256 × 256×256), (2) motion correction using a rigid brain transformation (six degrees of freedom), (3) stereotaxic Montreal Neurological Institute (MNI) normalization (Brett et al., 2002) using a landmark-based deformation approach (Müller and Kassubek, 2013), (4) temporal demeaning and linear detrending, (5) temporal bandpass filter (0.01 < f < 0.08 Hz), and (6) spatial smoothing using a 7 mm 3-dimensional full-width at half maximum (FWHM) Gaussian blur filter (LANDSCAPE Consortium et al., 2016; LANDSCAPE Consortium et al., 2015).

Functional data analysis

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Ten spherical a-priori-defined bilateral seed regions (r = 1 mm) were chosen based on the anatomical structures in order to compute functional connectivity maps of the motor system and the visual association system (reference) as follows: (1) primary motor system (seed voxels; MNI coordinates (x, y, z):±15, 30, 69), primary somatosensory cortex (±15,–30, 69), dorsolateral prefrontal association cortex (±22, 7, 55), Thalamus (±12,–29, 0), and visual association cortex (±51,–70, −13).

Functional connectome reconstruction

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We computed a functional connectome of the motor and visual association system using the defined seed regions by computing pairwise region-to-region functional connectivity and by computing Pearson’s product moment correlation coefficient (r-value) pairwise between voxel time series. Correlation coefficient was Fisher's r- to z-transformed to normally distributed z(r) score for further statistical analysis.

Interference statistics

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Overall functional connectivity was computed and used as a regressor (van den Heuvel et al., 2017) prior to subjecting the data to a two-sided parametric Student's t-test in order to test for pairwise differences between ALS patients and controls using the network-based statistics approach (Zalesky et al., 2010).

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Decision letter

  1. Moritz Helmstaedter
    Reviewing Editor; Max Planck Institute for Brain Research, Germany
  2. Richard B Ivry
    Senior Editor; University of California, Berkeley, United States
  3. Moritz Helmstaedter
    Reviewer; Max Planck Institute for Brain Research, Germany
  4. Gretchen Thomsen
    Reviewer
  5. Edward Stern
    Reviewer; Bar-Ilan University, Israel

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Stage-dependent hyperconnectivity to primary motor cortex in ALS mouse model revealed via retrograde- AAV9" for consideration by eLife. Your article has been reviewed by four peer reviewers, including Moritz Helmstaedter as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Richard Ivry as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Gretchen Thomsen (Reviewer #3); Edward Stern (Reviewer #4).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

This manuscript reports changes in the amounts of labeled axons projecting to the primary motor area in a mouse model of ALS and combines these results with fMRI data from human patients. This work emphasizes the need to consider the upper motor neurons in the SOD model of ALS.

While the reviewers were positive about the work, they agreed on a list of important issues that would need to be addressed in the revised manuscript:

1) Validity of the correspondence claims between Human and mouse data (see comments by reviewer #2 below).

2) Methodological caveats with interpreting the viral tracing data: need for reporting of absolute numbers, blinding of the analysis (see comments by all reviewers); comparison to other methods, justification of development of a new virus compared to e.g. Tervo et al., 2016.

3) General overstatement of "connectivity" while the experiments only provided axonal labeling. Here we would request a condensed revision that avoids overstatements as to the equivalence of "connectivity" with the number or density of axonal projections.

4) Statistical concerns (especially reviewer #4).

5) Lack of discussion of the limitations of the chosen tracing method: inability to analyse interneuron projections, lack of functional correlates (here we do not consider additional experiments necessary, but a more careful presentation and extended discussion.)

6) Relevance/interpretation of the spine data (Figure 6, see reviewer #2).

Reviewer #1

1) First, a key caveat of viral axonal tracing is the rate of infection. In order to allow a serious judgment of the data it is therefore critical to report raw neuron numbers first before a further processing of the data in terms of fractions, normalized numbers etc. can take place. Here, Figure 2 does not currently allow judging of the number of labeled presynaptic neurons. None of the images provides a resolution that allows counting neurons. The figure legend does not allow understanding of what is shown in panel B. Is this actual data, (i.e. numbers of neurons?) or just a sketch. The fractions reported in all panels are probably percent, this label is missing but here it is excessively important to report total numbers.

2) Furthermore, when the authors turn to the effect in the disease model, Figure 3, they switch to a different reporting axis, namely normalized neuron count. This is not clear why this is necessary, I understand that this is a normalization to the injection volume, but it leaves a certain amount of doubt whether this was necessary compared to reporting fractions of neurons as done before. I think it is necessary to first report all the raw numbers and then make very clear how the further processing of these numbers took place. Especially, since the effect is very strong and surprising as shown in Figure 3A, it is very important to understand whether this is driven by primarily the raw data or rather further enhanced by the normalization procedure that was used.

3) Third, the problem that figure legends are not overly informative and figure axes are sometimes not properly labeled is prevalent throughout the manuscript, for instance Figure 2—figure supplement 1, it is not made clear that this is likely the percentage of neurons from thalamus, otherwise the numbers wouldn't add up again (percent sign is missing). Again, absolute numbers would be very helpful together with more raw data images at high resolution as to allow the reader to judge the raw data in this work.

4) In the analysis of axonal projections from PMO, Figure 8, the analysis is carried out by reporting axonal volume. While this a plausible measure because it can be estimated from images, it is not extremely informative in quantitative terms. Furthermore, there are substantial issues in how to define the volume outlines giving different intensity levels of images. Here, at least a more thorough justification of this quantification and an attempt to relate this to the amount of connectivity would be needed. It is very unlikely that axonal volume directly correlates with the number of target neurons. Since the manuscript is otherwise quantitative about projections at the single neuron level it is surprising to the reader that this measure is of such coarse resolution here.

5) In this setting of small n of animals (3 versus 6) it is very important that the counting of neurons was blinded to the disease condition. I was not able to see in the methods that this was in fact carried out in a blinded fashion (the statement reads "the experimenter was kept blind to the genotype till the analysis stage", Materials and methods section – does this include the image analysis, i.e. neuron counting, injection volume normalization etc.?). It should include these steps.

Reviewer #2

1) I am not sure why the authors claim, "the motor subnetwork in an ALS mouse model displays selective increase in projections from SS and sMO to pMO in a pattern that closely matches the connectivity measurement by fMRI in human patients". While they find alterations in anatomical projection densities in the mouse between specific brain regions (most specifically somatosensory and contralateral motor cortices), in humans the find increases in functional connectivity strength between the primary somatosensory cortices and between pMO and some prefrontal cortex areas. Perhaps there are analogies between brain regions that I do not know about, but the authors should discuss in how far these patterns "closely match". Perhaps, side-by-side schematics of pMO inputs and their relative strength under normal and ALS conditions in mice and humans can clarify what is "closely match[ing]" here.

2) The presence of an increased retrograde labeling density does not necessarily imply any specific functional pattern of connectivity – for example, it is well documented, e.g. in the peripheral motor system, that axonal projections that are chronically silenced would show sprouting, which could easily result in increased retrograde tracing without any accompanying increase in "functional connectivity". Thus, the functional predictions that can be supported by an anatomical pattern of connectivity are typically weak. In general, while I am a bit skeptical that it will be easy to "interpret" fMRI findings in sporadic human ALS based on data from the mouse SOD model; at least similar modalities should be compared, i.e. structural data obtained from mice and humans or functional measurements dine in both species – ideally both kinds of measurements in parallel. This is clearly not a (reasonable) revision request but illustrates my general doubt whether it is meaningful to combine widely disparate types of data from a specific genetic animal model of disease and from sporadic human patients.

3) I think the term "connectomics" should be avoided here – at least for the mouse no evidence of any "connectivity" is provided, and certainly, no "comparison at circuit level of ALS mouse model and human connectomics" was attempted or achieved.

4) I was not sure what purpose the spine analysis in input fields of pMO in Figure 6 served (it was bit opaque to me what the difference is between what is shown in Figure 5 vs. Figure 6A – the authors need to clarify what the difference is or combine in a meaningful way). These data are interesting in their own right, especially if in a cell-to-cell comparison, dissociation between spine loss and misfolded SOD content could be shown. However, I do not see what they explain here, especially as the patterns between local spine density and projection strength do not seem to correlate especially well. I wonder whether this belongs into another "story".

Reviewer #3

1) The interpretation of results is a little bit unclear because of the complication that the quantification of labeled cells by means of anterograde or retrograde infectivity depends on the number of cells originally taking up the virus. If there are smaller numbers of cell populations in the injected region due to SOD1 disease pathology, there will be fewer projection neurons labeled. Is there altered connectivity, or just fewer cells in the injection regions delivering the virus?

2) I am curious as to why there were no endpoint studies conducted and the latest timepoint was 90/105 days. This seems an obvious piece of information that is missing to observe connectivity encompassing the entire spectrum of disease.

Reviewer #4

1) I found the study to be extremely well done. However, I have some issues with the statistical analysis. In general, the authors are quite rigorous. They correctly set the p (α) value prior to the analysis and determine significance accordingly. However, they then proceed to ascribe larger significance levels to the results of various comparisons, which is simply incorrect. The p value is the one which was set a priori: in this case 0.05. However, the authors are correct in trying to emphasize stronger effects. For this, I strongly recommend leaving the p values at 0.05, starred with an asterisk as done in the paper, and adding a statistic called Effect Size. There are several ways of calculating this, and the authors need pick the most appropriate (with, of course, citation of the exact method used in the methods section). These calculations will not take much time, and, in my opinion, greatly improve the paper. Especially for papers with great clinical significance, as I believe this study to be, this statistic is of increasing importance.

A good article detailing the procedures can be found here:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444174/

2) Another issue that concerns me is the specific types of changes in the distributions of the projections to and from the motor areas. The authors use mean and standard deviations throughout the paper, which, though reasonable, leave open a few questions. The entire study is about changes in the distributions of the projections. Mean and standard deviation alone are adequate, depending on the type of distributions. To give an idea, the authors should include the medians alongside the means. Usually, of course, the statistics will be similar. But, for example, if the medians are consistently above the means, that also tells us something about the shape of the distributions of the projections. And if that effect grows or shrinks in the Tg cases, that tells us about the effect of the pathology on the distributions. Of course, there are other methods for more sophisticated analysis of the changes in the distributions. Calculations of skewness and kurtosis come to mind. But this would be a method that yields a great deal of information without too much additional time and effort on the part of the authors. I emphasize that even if the distributions are completely normal, with no significant differences between means and medians, these statistics should be included in the paper: the information is of value.

3) Figure 3D: Regarding the reduction in percentage of projecting neurons of the SOD mice: While the changes in the mean are clear, a feature that seems to be overlooked is the reduction in variability in the Tg samples. While the statistical significance of the effect is not in doubt, the reason for the effect is of interest. The reduction in SD looks greater than one would expect from the properties of the distribution. I would ask the authors to include a table of coefficients of variation (SD/mean) of the 24 groups in the figure. Perhaps a statistical comparison of the CVs would also be appropriate. If there is a significant lowering of the CV in some the SOD cortex groups, this means that a significant reduction of the variability of this group is present. This might mean that some particular subpopulation of projections is eliminated, as opposed to a random reduction.

https://doi.org/10.7554/eLife.36892.036

Author response

Reviewer #1

1) First, a key caveat of viral axonal tracing is the rate of infection. In order to allow a serious judgment of the data it is therefore critical to report raw neuron numbers first before a further processing of the data in terms of fractions, normalized numbers etc. can take place. Here, Figure 2 does not currently allow judging of the number of labeled presynaptic neurons. None of the images provides a resolution that allows counting neurons. The figure legend does not allow understanding of what is shown in panel B. Is this actual data, (i.e. numbers of neurons?) or just a sketch. The fractions reported in all panels are probably percent, this label is missing but here it is excessively important to report total numbers.

We have followed the recommendations of the reviewer and thoroughly revised the manuscript and the data presented. In detail:

- We now present the raw number of projecting neurons to pMO or sMO in Figure 2; we had previously reported only the fraction (% of total number of neurons projecting to MO) represented by each structure in order to highlight their relative contribution to the total input to MO structures. We have now moved the graph depicting the fractions to the corresponding supplementary figure, in keeping with the suggestion to depict the raw numbers first.

- We now provide new representative pictures for Figure 2: a low-magnification overview picture of the brain section, accompanied by several high-resolution confocal panels depicting specific regions of interest (thalamic nuclei, primary somatosensory, contralateral motor cortex); in the high-resolution images single neurons can be easily identified.

- The panel B was intended to be a graphical depiction of the experimental design (injection site, regions in which projecting neurons have been analyzed) rather than a quantitative summary of the experiment. We have maintained the graphical depictions, clearly stating their purpose, and we have added small tables summarizing the experimental details. In a set of supplementary tables now we present the raw data corresponding to the normalized data represented in the main figure, to allow the comparison; all trends detectable in the raw-counts graphs are also detectable in the normalized-counts graphs although in the former, being the dataset more noisy (since one source of variability, namely the size of the injection site, has not been removed), some differences do not reach statistical significance. In addition, we provide the numerical data used to derive each graph as accompanying excel files, and we also provide the statistical details (mean, median, standard deviation, skewness, effect size, coefficient of variation and statistical significance) for each panel as supplementary tables.

- We have expanded the labels of every graph to include more details about the type of data depicted (raw counts, percentages, normalized counts) and we have stressed the type of processing (raw counts, normalized counts) also in the figure legends.

2) Furthermore, when the authors turn to the effect in the disease model, Figure 3, they switch to a different reporting axis, namely normalized neuron count. This is not clear why this is necessary, I understand that this is a normalization to the injection volume, but it leaves a certain amount of doubt whether this was necessary compared to reporting fractions of neurons as done before. I think it is necessary to first report all the raw numbers and then make very clear how the further processing of these numbers took place. Especially, since the effect is very strong and surprising as shown in Figure 3A, it is very important to understand whether this is driven by primarily the raw data or rather further enhanced by the normalization procedure that was used.

In the first version of the manuscript, we displayed the fraction of the total population of neurons projecting to pMO and sMO in Figure 2 to better depict the relative contribution of each structure to the total input, and we switched to normalized counts in Figure 3 to comparatively show that the number of neurons projecting to pMO was different in mSOD vs WT. We have now revised all figures to depict normalized values in the main figures and we provide the raw-counting (before normalization) in corresponding supplementary figures; for Figure 2, we also provide supplementary graphs depicting, for each structure, the fractional contribution to the overall pool of neurons projecting to pMO or sMO. In addition, we provide not supplementary tables in which the actual values (both before and after normalization), together with the statistical descriptors and the statistical significance, are reported. Because variability in the volume of the injection site can be minimized under the best experimental procedures but cannot be eliminated (as also shown in the dataset of the Allen Brain Institute), normalization for the injection volume, as performed by Oh et al., 2014, has been used to reduce this source of variability in the dataset. The increased number of neurons projecting from SS to pMO is already clearly detectable in the raw data and remains statistically significant after injection volume normalization and, with the exception of the increase in projections from cMOs to pMO at P30 (which is significant only upon normalization), all other differences are seen in the raw data as much as in normalized ones.

3) Third, the problem that figure legends are not overly informative and figure axes are sometimes not properly labeled is prevalent throughout the manuscript, for instance Figure 2—figure supplement 1, it is not made clear that this is likely the percentage of neurons from thalamus, otherwise the numbers wouldn't add up again (percent sign is missing). Again, absolute numbers would be very helpful together with more raw data images at high resolution as to allow the reader to judge the raw data in this work.

We have now thoroughly revised the figure legends and the details of the figures to ensure that the best description of the experimental design, of the results and of the graphs is achieved. In particular, for each graph legend we make clear the type of data depicted and the rationale for the use of that parameter; for the majority of the graphs, we provide now (in supplementary figures and tables) raw values, normalized values and/or percentages.

In the case of Figure 2—figure supplement 1, having presented in Figure 2 the share of neurons projecting to pMO located in thalamus, we broke down the number to highlight the contribution of each thalamic nucleus to that share; we have now provided both the raw number of neurons and the percentage of the total thalamic projection to pMO.

We have now added or substituted images using high-resolution confocal representative images.

4) In the analysis of axonal projections from PMO, Figure 8, the analysis is carried out by reporting axonal volume. While this a plausible measure because it can be estimated from images, it is not extremely informative in quantitative terms. Furthermore, there are substantial issues in how to define the volume outlines giving different intensity levels of images. Here, at least a more thorough justification of this quantification and an attempt to relate this to the amount of connectivity would be needed. It is very unlikely that axonal volume directly correlates with the number of target neurons. Since the manuscript is otherwise quantitative about projections at the single neuron level it is surprising to the reader that this measure is of such coarse resolution here.

In the initial version of the manuscript, we intended this piece of information to integrate the options for the interpretation of the fMRI-derived hyperconnectivity. However, we agree with the reviewer that, because of the intrinsic difference in the way anterograde and retrograde tracing experiments are quantified, the degree of resolution in the values cannot be compared. We therefore agree with the reviewer´s opinion that the dataset is more homogeneous, quantitative and streamlined without the anterograde tracing experiments and we have removed them from the revised version of the manuscript now resubmitted. We reserve to expand further the dataset on anterograde tracing in further studies. We have revised the text and the figures accordingly.

5) In this setting of small n of animals (3 versus 6) it is very important that the counting of neurons was blinded to the disease condition. I was not able to see in the methods that this was in fact carried out in a blinded fashion (the statement reads "the experimenter was kept blind to the genotype till the analysis stage", Materials and methods section – does this include the image analysis, i.e. neuron counting, injection volume normalization etc.?). It should include these steps.

The genotyping of the mouse was performed by a researcher different from the one performing the viral injection, the histology, the imaging and the image quantification, which includes the counting of all neurons. The genotype of each mouse was revealed only once the counting of the neurons and their anatomical annotation was closed and the corresponding file transferred to a different computer for the statistical analysis. At that point, individual mice were grouped according to the genotype. Incidentally, this design is the reason why the two groups have different n (6 vs 3), since mice were not pre-selected based on their genotype. We have now added more details to the description of the blinding design in the Materials and methods section.

Reviewer #2

1) I am not sure why the authors claim, "the motor subnetwork in an ALS mouse model displays selective increase in projections from SS and sMO to pMO in a pattern that closely matches the connectivity measurement by fMRI in human patients". While they find alterations in anatomical projection densities in the mouse between specific brain regions (most specifically somatosensory and contralateral motor cortices), in humans the find increases in functional connectivity strength between the primary somatosensory cortices and between pMO and some prefrontal cortex areas. Perhaps there are analogies between brain regions that I do not know about, but the authors should discuss in how far these patterns "closely match". Perhaps, side-by-side schematics of pMO inputs and their relative strength under normal and ALS conditions in mice and humans can clarify what is "closely match[ing]" here.

We have now detailed further our statement regarding (avoiding the use of the potentially misleading wording “closely matches” and referring to the human and mouse data as “sharing similarities”) the match between mouse and human data, underscoring that: (i) both in human and mouse data, we observe a progressive remodeling of the motor subnetwork (ii) both in (previously published) human data and current mouse data, the remodeling of the motor subnetwork is detectable much before the appearance of overt clinical or behavioural abnormalities (iii) both in human and mouse data, the remodeling of the motor subnetwork involves the primary motor cortex and more rostral, homolateral or controlateral structures (in the frontal lobe, prefrontal cortex or secondary motor cortex in human and mouse, respectively) and projecting to pMO iv) both in human and mouse data the primary somatosensory cortex appears involved in the motor subnetwork remodeling (either in terms of increased projection to pMO in the mouse, or in terms of inter-SS connectivity, connectivity in the sensorymotor cortex (close proximity prevents precise delineation of SS from pMO) and SS connectivity to other cortical areas (Agosta et al., 2011; Douaud et al., 2011); admittedly, the inter-SS projections were not investigated in our mouse dataset. Nevertheless, divergences were also observed, namely in the lack of hyperconnectivity from thalamus to pMO in ALS patients.

We agree with the reviewer that precise analogies between different cortical areas in mouse and humans are not always established, and we have now amended the discussion to include a detailed comparison of human and mouse data, highlighting the similarities and the possible divergences.

2) The presence of an increased retrograde labeling density does not necessarily imply any specific functional pattern of connectivity – for example, it is well documented, e.g. in the peripheral motor system, that axonal projections that are chronically silenced would show sprouting, which could easily result in increased retrograde tracing without any accompanying increase in "functional connectivity". Thus, the functional predictions that can be supported by an anatomical pattern of connectivity are typically weak. In general, while I am a bit skeptical that it will be easy to "interpret" fMRI findings in sporadic human ALS based on data from the mouse SOD model; at least similar modalities should be compared, i.e. structural data obtained from mice and humans or functional measurements dine in both species – ideally both kinds of measurements in parallel. This is clearly not a (reasonable) revision request but illustrates my general doubt whether it is meaningful to combine widely disparate types of data from a specific genetic animal model of disease and from sporadic human patients.

We accept the suggestion of the reviewer and we now provide a more balanced presentation of the goals that can be obtained by our work, not in terms of “interpreting” fMRI data but rather in terms of demonstrating that the large-scale circuit remodeling identified in functional human data can be detected in high-resolution structural data obtained in the mouse model and that the occurrence of the remodeling and similarities in the pattern can be drawn. Our data show that the motor subnetwork in the ALS model undergoes a progressive remodeling during disease progression, as also shown in fMRI data and therefore provide ground to further explore this phenomenon using other modalities.

Regarding the connection between structural and functional remodeling, we agree that this correlation is not always perfect. However, several studies have shown that the structure of cortical networks provides a strong template for functional connectivity: structurally connected regions are known to display high levels of functional connectivity, e.g. in the default brain network (Honey et al., 2007; Greicius et al., 2009) and the functional and structural connectivity maps are highly correlated (van den Heuvel et al., 2009). At large scale, a strong correlation between the degree of structural connectivity (defined by DTI measurements in white matter tracts) and of functional connectivity has been reported for the resting state network (Hagmann et al., 2008) as well as for resting and task-specific networks in aggregated as well as in single-individual data (Hermundstadt et al., 2013). Furthermore, the combination of anatomical tracing with fMRI measurements in non-human primates has shown that functional connectivity increases with structural connectivity (Shen et al., 2012) and the temporal stability of functional connectivity is highly correlated with the structural connectivity (Shen et al., 2015). On the other hand, functional or physiological data do not necessarily imply structural connectivity, since indirect connections may contribute to it, at least in part (Adachi et al., 2012). Thus, we now discuss the mouse findings, within the limits of extrapolating murine data to humans, as indicating one of the possible mechanisms involved in the genesis of hyperconnectivity, since structural connectivity may constraint functional connectivity pattern.

We also discuss the expansion of projection to pMO as non-mutually exclusive with other mechanisms that may involve in the increased connectivity (as seen in fMRI data). In particular, we discuss also the functional or anatomical dysfunction of inhibitory interneurons (Kew et al., 1993; Douaud et al. 2013) as a potential mechanism.

We have followed the reviewer´s suggestion on the mechanistic interpretation of the increased projection origin. In fact, we believe, in agreement with the he reviewer´s mention of the neuromuscular unit as model, that the peripheral motoneuron-muscle innervation may be a useful example in modeling sprouting: in fact, silencing of muscle cells (as obtained by blocking the neuromuscular junction with botulinum toxin or as occurring during denervation-reinnervation phenomena) leads to sprouting of the motoneurons projecting to the these cells through mechanisms driven to large extent (although not only) by the denervated cells (Caroni et al., 1994; Caroni et al., 1997); under the condition of denervation-induced sprouting, however the functional response of the muscle (since the definition of functional connectivity, which is based on the correlation of activity pattern, cannot be easily applied to the peripheral nervous system) is indeed altered, as shown by traditional emg studies, with a larger number of muscle cells recruited by the same motor neuron (and by changes in the temporal dynamics of compound motor unit potentials); so in the peripheral nervous system, increased sprouting resulting from reduced pre or post-synaptic activity leads to detectable changes in functional properties of the motor unit. We agree with the reviewer that our experimental data have not investigated whether the increase in projections originates as a consequence of post- or pre-synaptic abnormalities or may (unlikely) involve extensive synaptic silencing, a subject deserving a dedicated investigation; we now mention these mechanistic hypotheses in the Discussion section.

Moreover, we have now provided in the Introduction a more balanced description of the goals of our investigation in terms of the comparison of mouse vs human data, underscoring that, although the correlation between structural and functional connectivity is strong and repeatedly reported, the structural component is one of several factors determining the functional connectivity, and specifying that extrapolation from experimentally tractable models (such as the murine one) to humans must be accepted with caution.

3) I think the term "connectomics" should be avoided here – at least for the mouse no evidence of any "connectivity" is provided, and certainly, no "comparison at circuit level of ALS mouse model and human connectomics" was attempted or achieved.

We have amended the text to avoid reference to connectomics and we have re-phrased our statement to clarify that our data contribute to analysis of large-scale architecture of motor circuits in ALS mouse model and provide entry points to understand changes occurring in human patients.

4) I was not sure what purpose the spine analysis in input fields of pMO in Figure 6 served (it was bit opaque to me what the difference is between what is shown in Figure 5 vs. Figure 6A – the authors need to clarify what the difference is or combine in a meaningful way). These data are interesting in their own right, especially if in a cell-to-cell comparison, dissociation between spine loss and misfolded SOD content could be shown. However, I do not see what they explain here, especially as the patterns between local spine density and projection strength do not seem to correlate especially well. I wonder whether this belongs into another "story".

We now clarify that, having demonstrated the increased projections from SS and other cortical (and subcortical) structures at different stages of disease progression, we investigated if this phenotype was a consequence of pathogenic processes unfolding in the neurons projecting to pMO (i.e., if the neurons projecting from SS to pMO would display any sign of ongoing pathology which could be the driver of the increase in projections). We elected to monitor two readouts, namely the burden of misfolded SOD1 and the loss of spines, to determine if any pathology in the neurons projecting to pMO could justify the observed phenotype. The two readouts were selected in order to minimize the assumptions on the pathogenic process involved: whereas the burden of misfolded SOD1 appears to be very close to initial steps of the pathogenic cascade (the mutation of the very same gene is indeed pathogenic), the reduced spine density has been repeatedly identified in several neuronal subpopulations in ALS (e.g., Ozdinler et al., 2011; Fogarty et al., 2016, 2017) and offers a marker of the structural integrity of the neuron (which may be the result of the convergence of a number of ALS-related pathogenic processes, such as trafficking abnormalities, disturbed autophagy, altered cytoskeleton). We have demonstrated that the loss of spines does take place in the neurons projecting to pMO but there is no correlation between the increase in projections to pMO and the appearance of this sign of disease, suggesting that the increased-projection phenotype could not be a direct consequence of abnormalities (within the limits explored by the spine-loss readout) in projecting neurons. Of note, whereas loss of spines (in layer V neurons) was reported to occur in SS only at very advanced stages of the disease (Fogarty et al., 2016), in our subpopulation of neurons projecting to pMO this has been detected at earlier stages, underscoring the possibility of differential vulnerability of neuronal populations identified by their projections.

Regarding the specific points raised:

- We now present the data of Figure 6A first (neurons projecting to primary MO) and we have removed the data formerly in Figure 5 (neurons projecting to secondary MO) in order to focus on pMO data and streamline the presentation of the data.

- We now provide new data regarding the correlation between misfSOD burden and dendritic spine density (Figure 7 and Figure 8). We identified the subpopulation of pSS neurons projecting to pMO by injecting the latter with the retrograde AAV9 virus, and we assessed the burden of misfolded SOD1 by immunostaining; among the neurons projecting to pMO (ZsGreen+), we identified neurons with high misfSOD1 burden (fluorescence intensity>2000) and neurons with low misfSOD1 burden (fluorescence intensity < 1000). When we assessed the dendritic spines density for these two groups, we found that they were comparable and, in particular, the dendritic spine density in neurons in layer V (both in high-burden and in low-burden neurons) was significantly smaller than in layer II/III neurons (either high-burden either low-burden). Thus, these findings suggest that the loss-of-spines phenotype is dissociated from the misfolded SOD1 burden.

Reviewer #3

1) The interpretation of results is a little bit unclear because of the complication that the quantification of labeled cells by means of anterograde or retrograde infectivity depends on the number of cells originally taking up the virus. If there are smaller numbers of cell populations in the injected region due to SOD1 disease pathology, there will be fewer projection neurons labeled. Is there altered connectivity, or just fewer cells in the injection regions delivering the virus?

The determination of the number of distant neurons projecting to a given volume injected with the AAV suspension is allowed by the viral infection of the axons contained within the injection volume and it is therefore not directly dependent on the number of neurons whose cell body is located within the injection volume. In contrast, the number of target neurons located within the injection volume is a critical factor in experiments using monosynaptic-retrograde viruses such as replication-incompetent rabies (rabied-DeltaG). In our system, the density of neurons in the motor cortex would not affect the infectivity or the retrograde transport of the AAV per se, unless a severe decrease in the cell number in the injected region would cause the massive loss of projecting axons, due to the degeneration of their postsynaptic counterparts. However, we have consistently observed an expansion in the number of neurons (therefore, axons) projecting to the injection volume, indicating that a net loss of axons was not taking place. Loss of neurons may, speculatively, generate conditions that may increase axonal sprouting. We have now verified, using NeuN staining, that the neuronal density in pMO is comparable in WT and mSOD1 mice at least up to P90. Therefore, regarding projection to pMO, we believe that the changes in the number of projecting neurons are not due, or are not related, to loss of neurons in pMO. We have added the data relative to overall neuronal density in pMO in Figure 6—figure supplement 1 and we have amended the text accordingly.

2) I am curious as to why there were no endpoint studies conducted and the latest timepoint was 90/105 days. This seems an obvious piece of information that is missing to observe connectivity encompassing the entire spectrum of disease.

We agree with the reviewer that an evaluation at end-stage would have provided additional information; in order to maintain the same interval between injection and sacrifice as in other timepoints, mice for this end-stage assessment are to be injected at P115-120 and sacrificed at P130-135 (approximatively the anticipated average survival for this strain of SOD1(G93A) mice); at P115-120 this mice already display a severe motor impairment. In our preliminary investigation, we subject to intracerebral injections 3 SOD1(G93A) mice at P117, and none of them survived the procedure because of respiratory failure during the isoflurane anesthesia. Therefore, we believed unethical to proceed with experiments at timepoints in which very high mortality rate could be expected. We have added an explanation for the lack of a more advanced timepoint to the Results section.

Reviewer #4

1) I found the study to be extremely well done. However, I have some issues with the statistical analysis. In general, the authors are quite rigorous. They correctly set the p (α) value prior to the analysis and determine significance accordingly. However, they then proceed to ascribe larger significance levels to the results of various comparisons, which is simply incorrect. The p value is the one which was set a priori: in this case 0.05. However, the authors are correct in trying to emphasize stronger effects. For this, I strongly recommend leaving the p values at 0.05, starred with an asterisk as done in the paper, and adding a statistic called Effect Size. There are several ways of calculating this, and the authors need pick the most appropriate (with, of course, citation of the exact method used in the methods section). These calculations will not take much time, and, in my opinion, greatly improve the paper. Especially for papers with great clinical significance, as I believe this study to be, this statistic is of increasing importance.

A good article detailing the procedures can be found here:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444174/

We thank the reviewer for his suggestions. We have amended the graphs as recommended, highlighting with one asterisk the differences that are significant, irrespective of the value of p as far as p<0.05. In order to underscore the magnitude of the effects observed, we now report the effect size (calculated according to Cohen´s d approach) in a tabulated format so that for each structure whose projections to pMO are compared in WT and mSOD, both the Effect Size value and the statistical significance is available. In keeping with the requests of other reviewers, we provide the Effect Size value only in the tables in order to streamline the text and improve readability.

2) Another issue that concerns me is the specific types of changes in the distributions of the projections to and from the motor areas. The authors use mean and standard deviations throughout the paper, which, though reasonable, leave open a few questions. The entire study is about changes in the distributions of the projections. Mean and standard deviation alone are adequate, depending on the type of distributions. To give an idea, the authors should include the medians alongside the means. Usually, of course, the statistics will be similar. But, for example, if the medians are consistently above the means, that also tells us something about the shape of the distributions of the projections. And if that effect grows or shrinks in the Tg cases, that tells us about the effect of the pathology on the distributions. Of course, there are other methods for more sophisticated analysis of the changes in the distributions. Calculations of skewness and kurtosis come to mind. But this would be a method that yields a great deal of information without too much additional time and effort on the part of the authors. I emphasize that even if the distributions are completely normal, with no significant differences between means and medians, these statistics should be included in the paper: the information is of value.

We have now enriched the statistical analysis by providing the value of the median along with the mean for every structure considered, both for raw and normalized data; in order to improve readability and enable an easier comparison, we have provided the values in tabular format along with mean and standard deviation. The median values appear to be, with the exclusion of areas with small number of projecting neurons, very close (in most cases within 10%) of the mean value. In order to properly characterize the distribution, we have also computed the skewness for each group and we have tabulated the values along the mean, the median, the standard deviation and other statistical parameters. The skewness value was often close to 0 (in particular for structures with large number of projecting neurons) and in no case exceeding the value of ± 2.0 (the largest value observed being 1.7 for perirhinal cortex, in which only 25 pMO-projecting neurons were identified), indicating a limited impact of non-normal distribution on the results reported. We have amended the text in the methods section to provide a description of the calculation performed and a short summary of the interpretation of the Skewness values, and we have mentioned the median and skewness data in the Results section.

3) Figure 3D: Regarding the reduction in percentage of projecting neurons of the SOD mice: While the changes in the mean are clear, a feature that seems to be overlooked is the reduction in variability in the Tg samples. While the statistical significance of the effect is not in doubt, the reason for the effect is of interest. The reduction in SD looks greater than one would expect from the properties of the distribution. I would ask the authors to include a table of coefficients of variation (SD/mean) of the 24 groups in the figure. Perhaps a statistical comparison of the CVs would also be appropriate. If there is a significant lowering of the CV in some the SOD cortex groups, this means that a significant reduction of the variability of this group is present. This might mean that some particular subpopulation of projections is eliminated, as opposed to a random reduction.

We have now presented the data in Figure 3D in histogram format, in order to make the value of each column more easily compared, and we are providing all the numerical values, together with the value of the coefficient of variation, in a companion table. However, with the exclusion of the neurons allocated to layer iv (between 0 and 1.5%), the CV for all other cortical layers was comparable, preventing further analysis in this direction.

https://doi.org/10.7554/eLife.36892.037

Article and author information

Author details

  1. Barbara Commisso

    Department of Neurology, University of Ulm, Ulm, Germany
    Contribution
    Data curation, Formal analysis, Investigation, Visualization, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
  2. Lingjun Ding

    Department of Neurology, University of Ulm, Ulm, Germany
    Contribution
    Data curation, Investigation, Writing—original draft
    Competing interests
    No competing interests declared
  3. Karl Varadi

    Department of Internal Medicine III, University Hospital Heidelberg, Heidelberg, Germany
    Contribution
    Resources, Methodology, Writing—original draft
    Competing interests
    No competing interests declared
  4. Martin Gorges

    Department of Neurology, University of Ulm, Ulm, Germany
    Contribution
    Resources, Data curation, Investigation, Visualization, Writing—original draft
    Competing interests
    No competing interests declared
  5. David Bayer

    Department of Neurology, University of Ulm, Ulm, Germany
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
  6. Tobias M Boeckers

    Department of Anatomy and Cell biology, University of Ulm, Ulm, Germany
    Contribution
    Resources, Supervision, Writing—original draft, Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
  7. Albert C Ludolph

    Department of Neurology, University of Ulm, Ulm, Germany
    Contribution
    Supervision, Funding acquisition, Writing—original draft, Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
  8. Jan Kassubek

    Department of Neurology, University of Ulm, Ulm, Germany
    Contribution
    Conceptualization, Resources, Supervision, Writing—original draft, Project administration, Writing—review and editing
    Competing interests
    No competing interests declared
  9. Oliver J Müller

    Department of Internal Medicine, University of Kiel, Kiel, Germany
    Contribution
    Resources, Supervision, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
  10. Francesco Roselli

    1. Department of Neurology, University of Ulm, Ulm, Germany
    2. Department of Anatomy and Cell biology, University of Ulm, Ulm, Germany
    Contribution
    Conceptualization, Funding acquisition, Methodology, Writing—original draft, Project administration, Writing—review and editing
    For correspondence
    francesco.roselli@uni-ulm.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9935-6899

Funding

Deutsche Forschungsgemeinschaft (GRK1789)

  • David Bayer

Deutsche Forschungsgemeinschaft (SFB1149)

  • Francesco Roselli
  • Tobias M Boeckers

Ulm University Medical Faculty (Baustein program)

  • Francesco Roselli

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work has been supported by the Baustein program of Ulm University Medical Faculty, by the Deutsche Forschungs Gemeinschaft (DFG) as part of the Graduate School in Cellular and Molecular Mechanisms in Aging and by the Synapsis Foundation. FR is also supported by the DFG as part of the Collaborative Research Center 1149 ‘Danger Response, Disturbance Factors and Regenerative Potential after Acute Trauma’. This study was also supported by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG Grant Number LU 336/15–1), and the German Network for Motor Neuron Diseases (BMBF 01GM1103A). The authors thank the Ulm University Center for Translational Imaging MoMAN for its support. BC and DB are members of the International Graduate School in Molecular Medicine at Ulm University; DB is part of the Graduate School in Cellular and Molecular Mechanisms in Aging at Ulm University. The authors wish to acknowledge Mrs. Sonja Fuchs for MRI data acquisition of the human cohort; Ms. Silvia Cursano and Ms. Najwa Ouali Alami for the support and collaboration; Dr. Kim Moorwood and Mrs. Akila Chandrasekar for proofreading.

Ethics

Human subjects: All subjects included in the human study provided written informed consent according to institutional guidelines; the consent includes the declaration of the understanding of the study design, the agreement to the participation to the study, to the publication of the results, and to the data protection and anonymization procedures (under the chaptes Einwilligungserklaerung", "Probandeninformation", "Darstellung der Experimente", "Datenschutzerklärung"). The study was approved by the Ethics Committee of Ulm University, Ulm, Germany (reference #19/12) and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

Animal experimentation: All animal experiments have been approved by the Regierungpraesidium Tubingen under the licence no. 1312. All animals were handled according to the federal regulations on animal experimentations and under the supervision of the local veterinary office. Every effort was made to adhere to the 3R guidelines and to minimize suffering.

Senior Editor

  1. Richard B Ivry, University of California, Berkeley, United States

Reviewing Editor

  1. Moritz Helmstaedter, Max Planck Institute for Brain Research, Germany

Reviewers

  1. Moritz Helmstaedter, Max Planck Institute for Brain Research, Germany
  2. Gretchen Thomsen
  3. Edward Stern, Bar-Ilan University, Israel

Publication history

  1. Received: March 23, 2018
  2. Accepted: August 21, 2018
  3. Accepted Manuscript published: August 23, 2018 (version 1)
  4. Version of Record published: September 5, 2018 (version 2)

Copyright

© 2018, Commisso et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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