Spinal V1 inhibitory interneuron clades differ in birthdate, projections to motoneurons and heterogeneity

Reviewer #1 (Public Review):

To understand the spinal locomotor circuits, we need to reveal how various types of spinal interneurons work in the circuits. So far, the general roles of the cardinal groups of spinal interneurons (dI6, V0, V1, V2a, V2b, and V3) involved in locomotion have been roughly established but not fully understood. Each group is believed to contain some clades with more detailed functional differences. However, each character and function of these clades has not yet been elucidated.

In this study, Worthy et al. investigated clades of V1 neurons that are one of the main groups of inhibitory neurons in the spinal cord. Previous reports proposed four clades (Renshaw cells, FoxP2, sp8, and pou6f2) in V1 neurons defined by the expression of transcription factors. For V1 neurons in each of the four clades, the authors investigated the birth time and showed the postnatal location in the spinal cord according to the birth time. They found FoxP2-V1 located near LMC motor neurons that project to the limb. Using genetically labeled Foxp2-V1 mice, they showed that most of the synapses of V1 neurons on the cell bodies of motor neurons were from Foxp2-V1 and Renshaw cells. Furthermore, a higher proportion of Foxp2-V1 synapses is observed on LMC motor neurons than on axial motor neurons. They proposed that Foxp2-V1, which represents 60% of V1, can be further classified according to the expression of transcription factors Otp and Foxp4.

These results will be helpful for future analyses of the development and function of V1 neurons. In particular, the discovery of strong synaptic connections between Foxp2-V1 and LMC motor neurons will be beneficial in analyzing the role of V1 neurons in motor circuits that generate movement of the limbs.

The conclusions of this paper are well supported by the data obtained using widely used methods. However, for some analyses, the specificity of labeling V1 clades should be clearly described.

(1) In Figure 1, the MafB antibody (Sigma) was used to identify Renshaw cells at P5. However, according to the supplementary Figure 3D, the specificity of the MafB antibody (Sigma) is relatively low. The image of MafB-GFP, V1-INs, and MafB-IR at P5 should be added to the supplementary figure. The specificity of MaFB-IR-Sigma in V1 neurons at P5 should be shown. This image also might support the description of the genetically labeled MafB-V1 distribution at P5 (page 8, lines 28-32).

(2) The proportion of genetically labeled FoxP2-V1 in all V1 is more than 60%, although immunolabeled FoxP2-V1 is approximately 30% at P5. Genetically labeled Otp-V1 included other non-FoxP2 V1 clades (Fig. 8L-M). I wonder whether genetically labeled FoxP2-V1 might include the other three clades. The authors should show whether genetically labeled FoxP2-V1 expresses other clade markers, such as pou6f2, sp8, and calbindin, at P5.

Reviewer #2 (Public Review):

Summary:

This work brings important information regarding the composition of interneurons in the mammalian spinal cord, with a developmental perspective. Indeed, for the past decades, tools inspired from developmental biology have opened up promising avenues for challenging the functional heterogeneity in the spinal cord. They rely on the fact that neurons sharing similar mature properties also share a largely similar history of expression of specific transcription factor (TF) genes during embryogenic and postnatal development. For instance, neurons originating from p1 progenitors and expressing the TF Engrailed-1, form the V1 neuronal class. While such "cardinal" neuronal classes defined by one single RF indeed share numerous features - e.g., for the case of V1 neurons, a ventral positioning, an inhibitory nature and ipsilatetal projections - there is accumulating evidence for a finer-grained diversity and specialization in each class which is still largely obscure. The present work studies the heterogeneity of V1 interneurons and describes multiple classes based on their birthdate, final positioning, and expression of additional TF. It brings in particular a solid characterization of the Foxp2-expressing V1 interneurons for which authors also delve into the connectivity, and hence, possible functional implication. The work will be of interest to developmental biologists and those interested in the organization of the locomotor spinal network.

Strengths:

This study has deeply analyzed the diversity of V1 neurons by intersecting multiple criteria: TF expression, birthdate, location in the spinal cord, diversity along the rostro-caudal axis, and for some subsets, connectivity. This illustrates and exemplifies the absolute need to not consider cardinal classes, defined by one single TF, as homogeneous. Rather, it highlights the limits of single-TF classification, and exemplifies the existence of further diversity within cardinal class.

Experiments are generally well performed with a satisfactory number of animals and adequate statistical tests.

Authors have also paid strong attention to potential differences in cell-type classification when considering neurons currently expressing of a given TF (e.g., using antibodies), from those defined as having once expressed that TF (e.g., defined by a lineage-tracing strategy). This ambiguity is a frequent source of discrepancy of findings across studies.

Furthermore, there is a risk in developmental studies to overlook the fact that the spinal cord is functionally specialized rostro-caudally, and to generalize features that may only be applicable to a specific segment and hence to a specific motor pool. While motoneurons share the same dorso-ventral origin and appear homogenous on a ChAT staining, specific clusters are dedicated to specific muscle groups, e.g., axial, hypaxial or limb muscles. Here, the authors make the important distinction between different lumbar levels and detail the location and connectivity of their neurons of interest with respect to specific clusters of MN.

Finally, the authors are fully transparent on inter-animal variability in their representation and quantification. This is crucial to avoid the overgeneralization of findings but to rather provide a nuanced understanding of the complexities of spinal circuits.

Weaknesses:

The current version of the paper is VERY hard to read. It is often extremely difficult to "see the forest for the trees" and the reader is often drowned in methodological details that provide only minor additions to the scientific message. Non-specialists in developmental biology, but still interested in the spinal cord organization, especially students, might find this article challenging to digest and there is a high risk that they will be inclined to abandon reading it. The diversity of developmental stages studied (with possible mistakes between text and figures) adds a substantial complexity in the reading. It is also not clear at all why authors choose to focus on the Foxp2 V1 from page 9. Naively, the Pou6f2 might have been equally interesting. Finally, numerous discrepancies in the referencing of figures must also be fixed. I strongly recommend an in-depth streamlining and proofreading, and possibly moving some material to supplement (e.g. page 8, and elsewhere).

Second, and although the different V1 populations have been investigated in detail regarding their development and positioning, their functional ambition is not directly investigated through gain or loss of function experiments. For the Foxp2-V1, the developmental and anatomical mapping is complemented by a connectivity mapping (Fig 6s, 8), but the latter is fairly superficial compared to the former. Synapses (Fig 6) are counted on a relatively small number of motoneurons per animal, that may, or may not, be representative of the population. Likewise, putative synaptic inputs are only counted on neuronal somata. Motoneurons that lack of axono-somatic contacts may still be contacted distally. Hence, while this data is still suggestive of differences between V1 pools, it is only little predictive of function.

Third, I suggest taking with caution the rabies labelling (Figure 8). It is known that this type of Rabies vectors, when delivered from the periphery, might also label sensory afferents and their post-synaptic targets in the cord through anterograde transport and transneuronal spread (e.g., Pimpinella et al., 2022). Yet I am not sure authors have made all controls to exclude that labelled neurons, presumed here to be premotoneurons, could rather be anterogradely labelled from sensory afferents.

Fourth, the ambition to differentiate neuronal birthdate at a half-day resolution (e.g., E10 vs E10.5) is interesting but must be considered with caution. As the author explains in their methods, animals are caged at 7pm, and the plug is checked the next morning at 7 am. There is hence a potential error of 12h.

Reviewer #3 (Public Review):

Building on their previous work that defined four major subgroups, or clades, of V1 interneurons largely by their transcriptional signatures, they do meticulous yet comprehensive analysis of the birth timing of V1 interneurons by clade, and even intra-clade, subtypes. This analysis establishes new relationships between the molecular identity, settling position, and birth time with extraordinary precision.

These relationships are then explored from the lens of synaptic connectivity. Focusing on the FoxP2 clade, they show tight spatial correspondence between V1 and motor neuron position, and through detailed synaptic analysis, find the FoxP2 V1 clade, as compared to Renshaw cells and other V1s, are the major contributors to V1-to-limb motor neuron connectivity. Finally, by analyzing sensory-to-V1 connectivity too, they show that the FoxP2 clade exhibits Ia-reciprocal interneuron-like convergence of proprioceptive and Renshaw cell synapses.

Taking the development and connectivity analysis together, their work substantially advances our understanding of spinal interneurons and yields fundamental basic information about how cell type heterogeneity corresponds across developmental, molecular and anatomical features.

An additional strength of this study is that they generate new genetic tools for labeling interneuron subpopulations, and provide insider knowledge into antibody, genetic and viral labeling that often get tucked under the rug, providing a very useful resource for further studies.

My only criticism is that some of the main messages of the paper are buried in technical details. Better separation of the main conclusions of the paper, which should be kept in the main figures and text, and technical details/experimental nuances, which are essential but should be moved to the supplement, is critical. This will also correct the other issue with the text at present, which is that it is too long.

Author response:

We thank the reviewers and editors for their time and effort reviewing and improving this manuscript. We also thank them for their support.

Following the guidelines received by eLife we submit here the preliminary author’s response to the Public review with our planned changes to the manuscript.

Reviewer 1.

Comment 1. Issue on cross-reactivities of MafB antibodies.

We are confident that our description of MafB V1 interneurons is correct despite some cross-reactivity with one of the antibodies used. We test all antibodies we use, and unfortunately, we found an inverse relationship between sensitivity and specificity with the two MafB antibodies used in this study. We chose for quantification the one with highest sensitivity, despite the presence of some cross-reactivity in interneurons other than the dorsal and ventral (Renshaw) V1 populations we focus on. The dorsal and ventral (Renshaw) V1 populations we describe here are also reactive with the more specific antibody (although with lower sensitivity) and both are neatly labeled in a MafB-GFP reporter mouse as described in Figure 3. We will add an image to the supplement with MafB-GFP V1 Interneurons at P5 showing the immunoreactivity of both MafB antibodies as suggested by the reviewer. We agree with the reviewer that this will give further support to the characterization of these populations by either immunocytochemical or genetic means at P5.

Unfortunately, we cannot show lack of immunoreactivity for MafB antibodies in MafB GFP/GFP knockout mice at P5 because MafB global KOs die at birth as a result of respiratory failure. This is due to removal of inhibitory interneurons in brainstem centers critical for respiration (Blanchi at al. 2003 MafB deficiency causes defective respiratory rhythmogenesis and fatal central apnea at birth. Nat Neurosci. 6(10):1091-100. doi: 10.1038/nn1129. PMID: 14513037). This is why we used tissues from late embryos for testing antibody specificity in KO spinal cords. We will make this clearer in the text.

Comment 2. Overlap of V1 clades with lineage labeled Foxp2-V1s at P5.

We collected the data requested by the reviewer for P5 Foxp2-V1 interneurons and this will be added to an updated version of this figure. In comparison to the results with the OTP mouse, we only found marginal overlap at P5 with Renshaw cells, Pou6f2, and Sp8 V1s in our genetic intersection to label Foxp2-V1s. We apologize for not showing the data. We will make this clearer.

Reviewer 2.

Comment 1. Paper VERY hard to read.

We will make every effort to make the paper more readable by moving methodological discussions to supplementary materials. We strive to keep our methods as rigorous, clean, and replicable as possible, and that sometimes requires lengthy explanations of the details and reasoning behind our approaches. We will make sure this does not distract from the principal scientific messages we want to convey. We agree with the reviewer that these should be emphasized over methodological detail, and we will correct any mistakes in the text that lead to confusion. Thank you for pointing out this problem that we hope to correct in a new version. Why focus on Foxp2 V1s? We focus in the Foxp2 population for several reasons: 1) This is the largest population of V1s, and it is the one with a close spatial association to motoneurons, in particular limb motoneurons; 2) Given previous results (Benito-Gonzalez and Alvarez, 2012, cited in bibliography) it likely includes many reciprocal inhibitory interneurons; 3) We do not have the mice for studying the Pou6f2 (or Sp8) population, but similar studies are now being carried out in the Bikoff lab.

Comment 2. Lack of functional studies.

Functional studies are currently being carried out, both during development of limb function in postnatal mice as well as in adult animals. These studies required the creation of several new animal models and reagents. As with the present manuscript, we thoroughly characterize all animals and methods. This takes time and space. These studies are beyond the goals and length of the current manuscript, but we agree with the reviewer that these are the critical next experiments that need to be performed. We are now finalizing studies on the role of Foxp2-V1 interneurons in the postnatal development of limb coordination and validating approaches for silencing them in the adult while also optimizing behavioral assays and recordings. The data presented here on Foxp2-V1 interneuron heterogeneity and relations with limb motoneurons gives the necessary context for raising stronger hypotheses and aiding in the interpretation of future results in functional studies.

Synapse counts.

We respectfully disagree with the reviewer’s comments on our synapse density estimates. To fully explain the reasons and prevent any ambiguity, we need to focus on detailed methodological aspects. We apologize for the lengthy response. Two major issues were raised:

(1) Focus on the cell body.

The issue pointed by the reviewer of potential synapses in distal dendrites from V1 subgroups not projecting proximally was already discussed in the text. The reason we focus on the cell body is because 1) it is not feasible to study the full dendritic arbor of so many different types of motoneurons and 2) it allows us to identify V1 subpopulations that likely exert stronger modulation of motoneuron firing by targeting the proximal somatodendritic membrane. The fact that synaptic organization on motoneurons is similar on cell bodies and proximal dendrites (first 100 µm) suggests that inputs from V1 clades other than Renshaw cells are likely further away, and therefore there is limited benefit to include analyses of proximal dendrites in these data. Additionally, dendrites would be difficult to consistently follow in Chat immunostained tissue. We are currently using novel viral approaches to obtain labeling of single motoneurons and their full dendritic trees for more in depth dendritic analyses in the mouse. The classical method based on single cell in vivo intracellular labeling using micropipettes is presently very low yield in the adult mouse. We are experienced with detailed single motoneuron dendritic arbor analyses in cat and rat motoneurons (Alvarez et al. 1997 Cell-type specific organization of glycine receptor clusters in the mammalian spinal cord. J Comp Neurol. 379(1):150-70; Alvarez et al., 1998 Distribution of 5-hydroxytryptamine-immunoreactive boutons on alpha-motoneurons in the lumbar spinal cord of adult cats. J Comp Neurol. 393(1):69-83; Rotterman et al., 2014. Normal distribution of VGLUT1 synapses on spinal motoneuron dendrites and their reorganization after nerve injury. J Neurosci. 34(10):3475-92. doi: 10.1523/JNEUROSCI.4768-13.2014). Based on this experience, we do not believe it is feasible to include similar analyses to compare all motor columns throughout 6 segments of the spinal cord in this study. We agree with the reviewer that these are important data sets that need to be collected and they are planned for future experiments. These analyses will address different questions than the ones posed and answered in our current manuscript.

(2) Number of motoneurons analyzed.

We disagree with the reviewer assessment that our conclusions might be biased because of the numbers of motoneurons analyzed. We sampled a total of 295 motoneurons in 5 different mice (117 LMC/HMC, 99 MMC, and 79 PGC motoneurons), and we used stringent methods for synapse detection. Due to a technical error, Mouse 3 lacked data in upper lumbar and Th13, but all other mice included data in almost all motor columns and segments. We disagree with the characterization that these are small samples. For full transparency, all motoneurons analyzed were identified in Figure 6D. Each of the nearly 300 motoneuron cell bodies was carefully reconstructed through several optical planes to obtain an accurate estimate of synapse density. More automatic methods in current use in the literature sometimes analyze larger samples, but our methods are designed to avoid methodological biases inherent to these automatic methods. We do not use image thresholding to extract synaptic contacts because they lack accuracy identifying single synapses. Thus, estimates using this technique frequently refer to coverage, not synapse density. In addition, it is hard to keep threshold criteria consistent across multiple optical planes to analyze enough section thickness to estimate a motoneuron surface. This is because tissue light diffraction alters thresholding levels continuously across optical planes. Thus, many authors present data as linear densities across a perimeter (in a single plane) measuring many cells in one field in one plane. We avoid cell body linear densities (or coverage) because they bias counts towards larger synapses that have higher probability of being present at any single confocal plane. Moreover, estimates along a surface reduces synapse sampling variability and better estimate synaptic coverage compared to estimates derived from analyzing single cross-sections. We also confirm each genetically labeled varicosity as a likely synapse by accumulation of VGAT. In this manner we restrict our counts to synaptic boutons and not axons or intervaricose regions. Previously, we used bassoon to show the accuracy of our methods (Wootz et al. 2013 Alterations in the motor neuron-Renshaw cell circuit in the Sod1(G93A) mouse model. J Comp Neurol. 521(7):1449-69. doi: 10.1002/cne.23266). That means that our densities are true synaptic densities, which are difficult to extract from automatic methods that estimate fluorescence coverage over larger samples of somatic profiles but fail to individualize synapses and frequently bias results. These bulk methods introduce significant confounds in data interpretation: Is higher coverage due to bigger synapses or more synapses? Do threshold structures represent true synapses or also include axons? To what extent does sub- or over-thresholding in different planes affect identification of structures in contact with the motoneuron surface? We avoid all these problems. Not surprisingly, a nested ANOVA demonstrated consistent significant differences among motor columns and segments.

In summary, while more automatic methods allow larger samples, they disregard true synaptic densities and are based on thresholding methods with high variability in different motoneurons, optical planes and histological sections, thereby they require much larger numbers of motoneurons to overcome their many biases and sources of error. This is not our case. Our sample size is large enough considering the accuracy of our methods and data quality. This is demonstrated by consistency in statistical results across motor columns in different segments and mice.

Comment 3. Possibility of anterograde transsynaptic labeling from primary afferents infected with rabies virus.

This is a fair question that we did not clearly explain. The reviewer compares our results with those of Pimpinella et al., 2022. The methods used are different. To obtain anterograde tracing, these authors used Cre lines to achieve high levels of expression of TVA and RV glycoprotein in specific subtypes of sensory neurons including proprioceptors. Then EnVa-coated Rabies virus was injected directly inside the spinal cord for cell-type specificity. This method transynaptically labeled in the anterograde direction interneurons receiving inputs from specific types of sensory afferents, but the method does not have the muscle specificity required in our analyses. In our case, we used intramuscular injections at P5 of AAV1-G for transcomplementation with Rabies virus delta G injected in the same muscles later, at P15. In previous studies in which we used the RV-delta G virus without AAV1G, we analyzed motoneuron and primary afferent infection rates and found both to be considerably reduced with injection age. In our hands, there is almost no RV infection of primary afferents when Rabies virus is injected i.m. at P15, but there is some limited motoneuron infection remaining (that we used to our advantage in this paper to avoid primary afferent and developmental confounds).

Unfortunately, these methodological studies are presently communicated only in abstract form (GomezPerez et al., 2015 and 2016; Program Nos. 242.08 and 366.06). Therefore, we will add to the supplementary information some images from serial sections to those illustrated in the paper and that will show a few “start” LG motoneurons that remained labeled at this survival time point and the lack of any dorsal horn primary afferent labeling. This is consistent with our yet unpublished data that is based on a larger number of animals and more extensive time courses.

Comment 4. Temporal resolution of birth-dating.

We agree with the reviewer, and that is the reason we explicitly discuss that temporal resolution is not perfect (we also add a few more caveats that affect temporal resolution beyond the reviewers’ comments). However, the method is good enough to differentiate temporal sequences of neurogenesis with close to 12-hour resolution, once enough animals are analyzed to compensate for methodological temporal overlaps. That is the reason for our Figure 1D.

Reviewer 3

Comment 1. Text is too long and main message buried in technical details.

We agree and similar to our response to the first comment of Reviewer 2, we will revise the writing to make it more straightforward while moving some of the information on methods and technical discussion to supplementary materials. As demonstrated by reviewer 2 comments, methodological discussions are still important to best interpret the data presented in this paper.