The vertebrate spinal cord contains neurons with many different functions. In the middle, there is the central canal containing cerebrospinal fluid. The wall of the central canal is lined with ciliated cerebrospinal fluid-contacting (CSF-c) cells in all vertebrates (Agduhr, 1922; Vigh et al., 2004). In the lamprey, many CSF-c neurons are GABAergic sometimes with co-transmitters as somatostatin, neurotensin or dopamine (Christenson et al., 1991; Dale et al., 1997; Jalalvand et al., 2014; Rodicio et al., 2008). CSF-c neurons located at the lateral aspect of the central canal wall coexpress somatostatin and GABA and send axonal processes to mechanosensitive edge cells on the lateral margin of the spinal cord. GABA/somatostatin-expressing CSF-c neurons have recently been demonstrated to act as pH and mechanosensors and be part of a pH homeostatic system. At any deviation from neutral pH their activity is increased, which in turn leads to a depression of motor activity (Jalalvand et al., 2016a; Jalalvand et al., 2016b), which should counteract the processes that have led to the pH deviation. The accurate regulation of pH in the nervous system is of critical importance, and the fact that the GABA/somatostatin-expressing CSF-c neurons respond readily to pH changes gives them a particular homeostatic role. The effect of lowering the pH is blocked by APETx2, a specific acid-sensing ion channel 3 (ASIC3) antagonist (Diochot et al., 2004), as is their mechanosensitivity. The mechanism by which the GABA/somatostatin-expressing CSF-c neurons cause a reduction of locomotion has been suggested to be through release of somatostatin, since the effect on motor activity is blocked by somatostatin antagonists (Jalalvand et al., 2016a). CSF-c cells in a more ventral location contain dopamine (Brodin et al., 1990; Schotland et al., 1996) and their function is yet unknown.

In the present study, these two classes of CSF-c neurons, and their respective role for the operation of the pH homeostatic system and for the fluid transport in the central canal of the spinal cord is investigated. By applying a novel multi-scale imaging approach, using a high-throughput imaging method with sufficient resolution to resolve individual cells and organelles, we have been able to unravel the mode of operation of these two subtypes of CSF-c neurons. Light-sheet microscopy is the method of choice for rapid and minimally invasive acquisitions of large portions of tissues, but with a compromised spatial resolution in favour of recording speed and minimal photo-bleaching. To overcome this problem, we combine light-sheet with expansion microscopy (ExLSM) (Düring et al., 2019), a technique that physically expands tissue samples by a procedure including sample-embedding in a polyacrylamide gel and ends with ~four- to fivefold larger transparent samples (Chen, 2015; Tillberg et al., 2016). The physical expansion of the sample allows imaging of large volumes of spinal cord tissue with single-cell resolution and access to fine spatial information. This imaging approach allows quantification of the relative abundance and spatial patterns of CSF-c neuronal subtypes with specific focus on somatostatin- and dopamine-expressing cells along the central canal.

To further understand the physiological role of dopamine- and somatostatin-expressing CSF-c neurons, we take advantage of stimulated emission depletion (STED) microscopy to profile their neurotransmitter spatial distribution inside CSF-c neurons. STED microscopy (Hell and Wichmann, 1994; Willig et al., 2006) reaches a spatial resolution of ~40 nm, which allows us to resolve single synaptic vesicles even when the organelles are densely packed. This approach can identify neurotransmitter-specific release of dense-core synaptic vesicles during basal activity and upon pH stimulation in both somatostatin- and dopamine-expressing CSF-c neurons.

To gain structural information at ~5–10 nm level, an additional spatial resolution increase is needed, which we demonstrate with the application of STED imaging on expanded spinal cord tissues (ExSTED) (Gao et al., 2018). ExSTED imaging features an effective lateral spatial resolution of <10 nm, and allowed us to obtain cell-type-specific structural insight, previously accessible only with electron microscopy, on the cilia subtypes in ciliated somatostatin- and dopamine-expressing CSF-c. The cilia symmetry differs between primary (sensory) cilia (9 + 0 microtubule duplets) and motile cilia (9 + 2 microtubule duplets) and could be investigated in the context of specific cell types thanks to the spatial and specificity abilities of ExSTED. We could show that dopamine CSF-c neurons have motile cilia that may contribute to the flow of the cerebrospinal fluid, whereas somatostatin CSF-c neurons instead have predominantly sensory cilia conveying pH and mechanosensitivity.

Overall, this study uses recently developed high-resolution imaging techniques adapted to profiling the CSF-c neurons in the spinal cord. This provides cell-type information down to the molecular level. The experimental design developed in this study can be applied also to other types of tissue.

In this study, we uncovered the dense spatial organization of somatostatin- and dopamine-expressing CSF-c neurons along the central canal. ExLSM allowed us to visualize and quantify CSF-c neurons in a large volume of the spinal cord with a fourfold increase in resolution, necessary to distinguish and count each individual cell. There were approximately twice as many somatostatin CSF-c neurons as dopaminergic CSF-c neurons along the whole length of the spinal cord. One area, at the level of the rostral part of the dorsal fin had, however, a somewhat larger number of somatostatin CSF-c neurons than elsewhere. This is the same spinal cord area in which a dramatic decrease occurs of the serotonin/dopamine neurons that are located just ventral to the central canal (Figure 8A, Schotland et al., 1995; Zhang et al., 1996). Whether this is a coincidence or a related phenomenon remains to be elucidated.

Figure 8

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Somatostatin and dopamine DCVs were quantified with STED microscopy (Figure 8B). The size distribution is in line with previous electron microscopy studies (Schotland et al., 1996). The STED data reveal the presence of DCVs in several CSF-c cellular subcompartments, including the bulb protrusion into the central canal, soma, and axonal branches. We could show a marked reduction of somatostatin DCVs number density in the soma of CSF-c neurons when exposed to acidic or alkaline pH, while there was no effect on the dopamine DCVs number density. This lack of change in the number density of DCVs in dopaminergic CSF-c neurons agrees with the present electrophysiological results showing that these cells are not activated by changes of the extracellular pH.

We have shown previously that somatostatin/GABA-expressing CSF-c neurons are responding to acidic and alkaline extracellular solutions (Jalalvand et al., 2016a; Jalalvand et al., 2016b) being part of a homeostatic system for reducing motor activity when the spinal cord/brain is exposed to changes of pH as due to metabolic or respiratory stress or intense muscle activity. We now show with STED imaging that the pH response resulted from somatostatin DCVs release, whereas GABA was unaffected.

At the ultrastructural level, GABAergic CSF-c neurons have been shown to have ciliated endings in the lamprey and other species (Orts-Del’Immagine et al., 2014; Schotland et al., 1996; Vigh et al., 2004). Since somatostatin/GABA and dopaminergic/GABA CSF-c neurons have similar responses to mechanical stimuli, while only the former responds to pH changes, the question arose if these two types of CSF-c neurons have different forms of cilia. To visualize cilia microtubule symmetry in CSF-c neurons a resolution of 5–10 nm is needed, which was provided by the combination of expansion and STED microscopy (ExSTED) (Gao et al., 2018). ExSTED microscopy allowed us to obtain cell-type-specific structural insights on the cilium types in ciliated somatostatin- and dopamine-expressing CSF-c neurons, respectively (Figure 8B). Using ExSTED microscopy we found primary cilia with 9 + 0 symmetry in somatostatin-expressing CSF-c neurons, also found in specialized sensory cells (Kirschen and Xiong, 2017; Singla and Reiter, 2006) and motile cilia with 9 + 2 symmetry in dopaminergic CSF-c neurons that may serve to generate fluid flow (Faubel et al., 2016; Satir and Christensen, 2007). The high expression of sensory cilia (9 + 0) found in pH/mechanosensitive somatostatin-expressing CSF-c neurons provide additional evidence to recognize them as sensory neurons. The mechanosensitivity of the dopamine- and somatostatin-expressing CSF-c neurons is mediated by different cellular mechanisms, the latter is blocked by APETx2, a selective ASIC3 blocker as shown in previous studies (Jalalvand et al., 2016a; Jalalvand et al., 2016b). The mechanosensitivity of the dopamine CSF-c neurons is, however, unaffected by APETx2. This blocker is specific to the ASIC3 but does not block ASIC1 receptors (Diochot et al., 2004), the latter having been cloned in lamprey (Coric et al., 2005) and found insensitive to pH. The data support the previous interpretation that ASIC3 mediates both the acid-sensing and the mechanosensitivity, in somatostatin-expressing CSF-c neurons (Jalalvand et al., 2016a; Jalalvand et al., 2016b). PKD2L1 channel is expressed in the dopaminergic CSF-c neurons (Figure 4—figure supplement 2) and this channel is known to serve as a mechanosensitive receptor (Kamura et al., 2011; Nauli et al., 2003) and may therefore serve in this function also for the dopaminergic CSF-c neurons. In the zebrafish, the mechanosensitive GABA CSF-c neurons sense the lateral bending movements occurring during locomotion through PKD2L1 channels (Fidelin et al., 2015). We have also observed expression of ASIC3 and PKD2L1 channels, as well as sensory cilia in CSF-c neurons of the mouse, which suggests that this may be representative of all vertebrates. The motile cilia (9 + 2) found only in the mechanosensitive (non-pH) dopaminergic CSF-c neurons may suggest that they may act as a CSF flow generator.

In conclusion, by applying ExM combined with light-sheet and STED microscopy with nanoscale precision, the spatial organization, abundance, and subcellular composition of two distinct GABAergic CSF-c neuronal subtypes in the lamprey spinal cord were elucidated. One type is sensitive to pH and displaying mechanosensitivity (somatostatin) while the other (dopamine) only responds to mechanical stimuli. They use different cellular mechanisms for the transduction of mechanical stimuli to their cilia.

Data availability The data that support the implementation of the method and support the findings in this study, including images and statistical analysis are openly available in Zenodo with reference number 5758671 at https://doi.org/10.5281/zenodo.5758671.

1. 1. Agduhr E

   (1922) Über ein zentrales Sinnesorgan (?) bei den Vertebraten

   Zeitschrift Für Anatomie Und Entwicklungsgeschichte 66:223–360.

   https://doi.org/10.1007/BF02593586

   • Google Scholar
2. 1. Alvelid J
   2. Testa I

   (2019) Stable stimulated emission depletion imaging of extended sample regions

   Journal of Physics D 53:024001.

   https://doi.org/10.1088/1361-6463/ab4c13

   • Google Scholar
3. 1. Atkinson KF
   2. Sherpa RT
   3. Nauli SM

   (2019) The Role of the Primary Cilium in Sensing Extracellular pH

   Cells 8:7.

   https://doi.org/10.3390/cells8070704

   • PubMed
   • Google Scholar
4. 1. Böhm UL
   2. Prendergast A
   3. Djenoune L
   4. Nunes Figueiredo S
   5. Gomez J
   6. Stokes C
   7. Kaiser S
   8. Suster M
   9. Kawakami K
   10. Charpentier M
   11. Concordet J-P
   12. Rio J-P
   13. Del Bene F
   14. Wyart C

   (2016) CSF-contacting neurons regulate locomotion by relaying mechanical stimuli to spinal circuits

   Nature Communications 7:10866.

   https://doi.org/10.1038/ncomms10866

   • PubMed
   • Google Scholar
5. 1. Brodin L
   2. Dale N
   3. Christenson J
   4. Storm-Mathisen J
   5. Hökfelt T
   6. Grillner S

   (1990) Three types of GABA-immunoreactive cells in the lamprey spinal cord

   Brain Research 508:172–175.

   https://doi.org/10.1016/0006-8993(90)91134-3

   • PubMed
   • Google Scholar
6. 1. Chen F

   (2015) Optical imaging Expansion microscopy

   Science 347:543–548.

   https://doi.org/10.1126/science.1260088

   • Google Scholar
7. 1. Christenson J
   2. Alford S
   3. Grillner S
   4. Hökfelt T

   (1991) Co-localized GABA and somatostatin use different ionic mechanisms to hyperpolarize target neurons in the lamprey spinal cord

   Neuroscience Letters 134:93–97.

   https://doi.org/10.1016/0304-3940(91)90516-v

   • PubMed
   • Google Scholar
8. 1. Coric T
   2. Zheng D
   3. Gerstein M
   4. Canessa CM

   (2005) Proton sensitivity of ASIC1 appeared with the rise of fishes by changes of residues in the region that follows TM1 in the ectodomain of the channel

   The Journal of Physiology 568:725–735.

   https://doi.org/10.1113/jphysiol.2005.087734

   • PubMed
   • Google Scholar
9. 1. Dale N
   2. Roberts A
   3. Storm-Mathisen J

   (1997) The morphology and distribution of ‘Kolmer–Agduhr cells’, a class of cerebrospinal-fluid-contacting neurons revealed in the frog embryo spinal cord by GABA immunocytochemistry

   Proceedings of the Royal Society of London. Series B. Biological Sciences 232:193–203.

   https://doi.org/10.1098/rspb.1987.0068

   • Google Scholar
10. 1. Diochot S
    2. Baron A
    3. Rash LD
    4. Deval E
    5. Escoubas P
    6. Scarzello S
    7. Salinas M
    8. Lazdunski M

    (2004) A new sea anemone peptide, APETx2, inhibits ASIC3, a major acid-sensitive channel in sensory neurons

    The EMBO Journal 23:1516–1525.

    https://doi.org/10.1038/sj.emboj.7600177

    • PubMed
    • Google Scholar
11. 1. Düring DN
    2. Rocha MD
    3. Dittrich F
    4. Gahr M
    5. Hahnloser RHR

    (2019) Expansion Light Sheet Microscopy Resolves Subcellular Structures in Large Portions of the Songbird Brain

    Frontiers in Neuroanatomy 13:2.

    https://doi.org/10.3389/fnana.2019.00002

    • PubMed
    • Google Scholar
12. 1. Faubel R
    2. Westendorf C
    3. Bodenschatz E
    4. Eichele G

    (2016) Cilia-based flow network in the brain ventricles

    Science 353:176–178.

    https://doi.org/10.1126/science.aae0450

    • PubMed
    • Google Scholar
13. 1. Fidelin K
    2. Djenoune L
    3. Stokes C
    4. Prendergast A
    5. Gomez J
    6. Baradel A
    7. Del Bene F
    8. Wyart C

    (2015) State-Dependent Modulation of Locomotion by GABAergic Spinal Sensory Neurons

    Current Biology 25:3035–3047.

    https://doi.org/10.1016/j.cub.2015.09.070

    • PubMed
    • Google Scholar
14. 1. Gaertig J
    2. Wloga D

    (2008) Ciliary tubulin and its post-translational modifications

    Current Topics in Developmental Biology 85:83–113.

    https://doi.org/10.1016/S0070-2153(08)00804-1

    • PubMed
    • Google Scholar
15. 1. Gao M
    2. Maraspini R
    3. Beutel O
    4. Zehtabian A
    5. Eickholt B
    6. Honigmann A
    7. Ewers H

    (2018) Expansion Stimulated Emission Depletion Microscopy (ExSTED)

    ACS Nano 12:4178–4185.

    https://doi.org/10.1021/acsnano.8b00776

    • PubMed
    • Google Scholar
16. 1. Hell SW
    2. Wichmann J

    (1994) Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy

    Optics Letters 19:780–782.

    https://doi.org/10.1364/ol.19.000780

    • PubMed
    • Google Scholar
17. 1. Jalalvand E
    2. Robertson B
    3. Wallén P
    4. Hill RH
    5. Grillner S

    (2014) Laterally projecting cerebrospinal fluid-contacting cells in the lamprey spinal cord are of two distinct types

    The Journal of Comparative Neurology 522:Spc1.

    https://doi.org/10.1002/cne.23584

    • PubMed
    • Google Scholar
18. 1. Jalalvand E
    2. Robertson B
    3. Tostivint H
    4. Wallén P
    5. Grillner S

    (2016a) The Spinal Cord Has an Intrinsic System for the Control of pH

    Current Biology 26:1346–1351.

    https://doi.org/10.1016/j.cub.2016.03.048

    • PubMed
    • Google Scholar
19. 1. Jalalvand E
    2. Robertson B
    3. Wallén P
    4. Grillner S

    (2016b) Ciliated neurons lining the central canal sense both fluid movement and pH through ASIC3

    Nature Communications 7:10002.

    https://doi.org/10.1038/ncomms10002

    • PubMed
    • Google Scholar
20. 1. Kamura K
    2. Kobayashi D
    3. Uehara Y
    4. Koshida S
    5. Iijima N
    6. Kudo A
    7. Yokoyama T
    8. Takeda H

    (2011) Pkd1l1 complexes with Pkd2 on motile cilia and functions to establish the left-right axis

    Development 138:1121–1129.

    https://doi.org/10.1242/dev.058271

    • PubMed
    • Google Scholar
21. 1. Kirschen GW
    2. Xiong Q

    (2017) Primary cilia as a novel horizon between neuron and environment

    Neural Regeneration Research 12:1225–1230.

    https://doi.org/10.4103/1673-5374.213535

    • PubMed
    • Google Scholar
22. 1. Nauli SM
    2. Alenghat FJ
    3. Luo Y
    4. Williams E
    5. Vassilev P
    6. Li X
    7. Elia AEH
    8. Lu W
    9. Brown EM
    10. Quinn SJ
    11. Ingber DE
    12. Zhou J

    (2003) Polycystins 1 and 2 mediate mechanosensation in the primary cilium of kidney cells

    Nature Genetics 33:129–137.

    https://doi.org/10.1038/ng1076

    • PubMed
    • Google Scholar
23. 1. Ollion J
    2. Cochennec J
    3. Loll F
    4. Escudé C
    5. Boudier T

    (2013) TANGO: a generic tool for high-throughput 3D image analysis for studying nuclear organization

    Bioinformatics 29:1840–1841.

    https://doi.org/10.1093/bioinformatics/btt276

    • PubMed
    • Google Scholar
24. 1. Orts-Del’Immagine A
    2. Kastner A
    3. Tillement V
    4. Tardivel C
    5. Trouslard J
    6. Wanaverbecq N

    (2014) Morphology, distribution and phenotype of polycystin kidney disease 2-like 1-positive cerebrospinal fluid contacting neurons in the brainstem of adult mice

    PLOS ONE 9:e87748.

    https://doi.org/10.1371/journal.pone.0087748

    • PubMed
    • Google Scholar
25. 1. Rodicio MC
    2. Villar-Cerviño V
    3. Barreiro-Iglesias A
    4. Anadón R

    (2008) Colocalization of dopamine and GABA in spinal cord neurones in the sea lamprey

    Brain Research Bulletin 76:45–49.

    https://doi.org/10.1016/j.brainresbull.2007.10.062

    • PubMed
    • Google Scholar
26. 1. Satir P

    (2005) Tour of organelles through the electron microscope: a reprinting of Keith R. Porter’s classic Harvey Lecture with a new introduction

    The Anatomical Record. Part A, Discoveries in Molecular, Cellular, and Evolutionary Biology 287:1184–1185.

    https://doi.org/10.1002/ar.a.20222

    • PubMed
    • Google Scholar
27. 1. Satir P
    2. Christensen ST

    (2007) Overview of structure and function of mammalian cilia

    Annual Review of Physiology 69:377–400.

    https://doi.org/10.1146/annurev.physiol.69.040705.141236

    • PubMed
    • Google Scholar
28. 1. Schindelin J
    2. Arganda-Carreras I
    3. Frise E
    4. Kaynig V
    5. Longair M
    6. Pietzsch T
    7. Preibisch S
    8. Rueden C
    9. Saalfeld S
    10. Schmid B
    11. Tinevez J-Y
    12. White DJ
    13. Hartenstein V
    14. Eliceiri K
    15. Tomancak P
    16. Cardona A

    (2012) Fiji: an open-source platform for biological-image analysis

    Nature Methods 9:676–682.

    https://doi.org/10.1038/nmeth.2019

    • PubMed
    • Google Scholar
29. 1. Schotland J
    2. Shupliakov O
    3. Wikström M
    4. Brodin L
    5. Srinivasan M
    6. You ZB
    7. Herrera-Marschitz M
    8. Zhang W
    9. Hökfelt T
    10. Grillner S

    (1995) Control of lamprey locomotor neurons by colocalized monoamine transmitters

    Nature 374:266–268.

    https://doi.org/10.1038/374266a0

    • PubMed
    • Google Scholar
30. 1. Schotland JL
    2. Shupliakov O
    3. Grillner S
    4. Brodin L

    (1996) Synaptic and nonsynaptic monoaminergic neuron systems in the lamprey spinal cord

    The Journal of Comparative Neurology 372:229–244.

    https://doi.org/10.1002/(SICI)1096-9861(19960819)372:2<229::AID-CNE6>3.0.CO;2-5

    • PubMed
    • Google Scholar
31. 1. Singla V
    2. Reiter JF

    (2006) The primary cilium as the cell’s antenna: signaling at a sensory organelle

    Science 313:629–633.

    https://doi.org/10.1126/science.1124534

    • PubMed
    • Google Scholar
32. 1. Sternberg JR
    2. Prendergast AE
    3. Brosse L
    4. Cantaut-Belarif Y
    5. Thouvenin O
    6. Orts-Del’Immagine A
    7. Castillo L
    8. Djenoune L
    9. Kurisu S
    10. McDearmid JR
    11. Bardet P-L
    12. Boccara C
    13. Okamoto H
    14. Delmas P
    15. Wyart C

    (2018) Pkd2l1 is required for mechanoception in cerebrospinal fluid-contacting neurons and maintenance of spine curvature

    Nature Communications 9:3804.

    https://doi.org/10.1038/s41467-018-06225-x

    • PubMed
    • Google Scholar
33. 1. Tillberg PW
    2. Chen F
    3. Piatkevich KD
    4. Zhao Y
    5. Yu C-CJ
    6. English BP
    7. Gao L
    8. Martorell A
    9. Suk H-J
    10. Yoshida F
    11. DeGennaro EM
    12. Roossien DH
    13. Gong G
    14. Seneviratne U
    15. Tannenbaum SR
    16. Desimone R
    17. Cai D
    18. Boyden ES

    (2016) Protein-retention expansion microscopy of cells and tissues labeled using standard fluorescent proteins and antibodies

    Nature Biotechnology 34:987–992.

    https://doi.org/10.1038/nbt.3625

    • PubMed
    • Google Scholar
34. 1. Vigh B
    2. Manzano e Silva MJ
    3. Frank CL
    4. Vincze C
    5. Czirok SJ
    6. Szabó A
    7. Lukáts A
    8. Szél A

    (2004) The system of cerebrospinal fluid-contacting neurons Its supposed role in the nonsynaptic signal transmission of the brain

    Histology and Histopathology 19:607–628.

    https://doi.org/10.14670/HH-19.607

    • PubMed
    • Google Scholar
35. 1. Vina E
    2. Parisi V
    3. Sánchez-Ramos C
    4. Cabo R
    5. Guerrera MC
    6. Quirós LM
    7. Germanà A
    8. Vega JA
    9. García-Suárez O

    (2015) Acid-sensing ion channels (ASICs) 2 and 4.2 are expressed in the retina of the adult zebrafish

    Cell and Tissue Research 360:223–231.

    https://doi.org/10.1007/s00441-014-2084-5

    • PubMed
    • Google Scholar
36. 1. Willig KI
    2. Rizzoli SO
    3. Westphal V
    4. Jahn R
    5. Hell SW

    (2006) STED microscopy reveals that synaptotagmin remains clustered after synaptic vesicle exocytosis

    Nature 440:935–939.

    https://doi.org/10.1038/nature04592

    • PubMed
    • Google Scholar
37. 1. Zhang W
    2. Pombal MA
    3. el Manira A
    4. Grillner S

    (1996) Rostrocaudal distribution of 5-HT innervation in the lamprey spinal cord and differential effects of 5-HT on fictive locomotion

    The Journal of Comparative Neurology 374:278–290.

    https://doi.org/10.1002/(SICI)1096-9861(19961014)374:2<278::AID-CNE9>3.0.CO;2-#

    • PubMed
    • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "ExSTED microscopy reveals contrasting functions of dopamine and somatostatin CSF-c neurons along the central canal" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by Suzanne Pfeffer as the Senior and Reviewing Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Francesca Bottanelli (Reviewer #1); Claire Wyart (Reviewer #2).

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

Essential revisions:

1. The introduction should give more insights into the underlying biology and also clarify which questions are being asked here: what is new and why these are important, to help the reader understand the biology that underlies this work.

2. The interpretation of physiological results based on established knowledge of ASICs in lamprey cannot be easily explained, and the authors need to provide further data to support their claims or rephrase their conclusions with great care. Specific details follow in the individual reviews below. Electrophysiological recordings of both SST+ and DA+ neurons is important to make conclusions related to the functional roles of these cells, as the microscopy alone cannot infer on the sensory function; alternatively the text needs to be modified in line with suitable conclusions from the morphological analyses.

3. Many of the figures were provided in suboptimal resolution and it is essential to be sure they are provided at the appropriate quality.

Reviewer #1 (Recommendations for the authors):

The work is generally well presented. However, some additional information could make it easier for the non-expert reader. Here are some suggestions:

• A model at the end to compile all results on pH sensitivity, mechanosensing, etc… would greatly simplify the understanding of the messages of the paper.

• TH staining is not explained.

• The authors claim a resolution below 10 nm however the size of the antibodies used for staining should be considered.

• The authors say "Somatostatin-expressing CSF-c neurons are found throughout the whole volume and located laterally relative to the central canal, while the dopaminergic CSF-c neurons are located ventrally" could the author point that out in figure 1 as I have a hard time visualizing it.

• Figure 1f = Cell number per volume? Or total cell number?

• Figure 3g = vesicles per volume? Or total amount of vesicles per soma? Additionally, the increase of number of vesicles in axon is not quantified?

• Figure 4d = same comment as for figure 3.

• Figure 5 = I am wondering why the expansion factor was calculated by measuring the gel slice rather than looking at a sub-cellular structure. Could the authors calculate the expansion factor by measuring cilia diameter in STED and expanded confocal samples?

Reviewer #2 (Recommendations for the authors):

1. On the microscopy section, the authors make a statement that DSVs within somatostatin-positive sensory cells are present in the axons at baseline and become more numerous upon pH variations. To illustrate their point, the authors need to show:

a) Images illustrating that DSVs were found in the axonal extension (page 5, "data not shown") at baseline, and;

b) Evidence determining whether DSVs are released or transported from the soma to the axon (Figure 3d-f) upon changes of pH. A quantification of the total number of DSV per cell could be insightful to resolve these 2 interpretations.

This point is important for interpretation of the mechanisms at play upon variations of pH.

2. The interpretation of physiological results based on established knowledge of ASICs in lamprey cannot be easily explained, and the authors need to provide further data to support their claims or rephrase their conclusions:

a) the authors need to show that somatostatin-positive cells respond to pH in their conditions as it is key for their interpretation, and their previous article on all spinal CSF-contacting neurons did not mention that only half the ciliated neurons contacting the CSF responded to pH (Jalalvand et al. Current Biology 2016).

b) Similarly, the authors here examine the mechanosensory response of dopaminergic CSF-contacting neurons without quantifying the response of somatostatin-positive neurons to the same stimulus. Both responses to variations of pH and mechanical stimulation need to be performed in both cell types.

c) Another group have shown that lamprey only express one type of ASIC channel, ASIC1 (not the channel ASIC3 ): see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3047259/). Therefore, how can the authors explain the effect of the drug used against ASIC3 in this context ?

In order to solve this issue, the authors could show expression of ASIC channels, and test the effect of a drug against ASIC1 on the response of somatostatin-expressing cells.

d) Finally, the only ASIC channel expressed in lamprey, ASIC1, when cloned and tested, has been reported to be proton insensitive (see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1464184/)--how then to explain then the activation of a subset of ciliated neurons exposed to protons?

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "ExSTED microscopy reveals contrasting functions of dopamine and somatostatin CSF-c neurons along the lamprey central canal" for further consideration by eLife. Your revised article has been reviewed by 3 peer reviewers and the evaluation has been overseen by Suzanne Pfeffer as the Senior and Reviewing Editor.

We would like to publish this story in eLife but ask you to please make textual changes in response to this reviewer's comments.

Specific comments from one reviewer:

The authors satisfactorily answered some of the issues raised on the quantification of images and provided high resolution images for their outstanding data acquired at high resolution. These data are very convincing and beautiful. Congratulations, this will be very precious and inspirational for further studies in the spinal field !

The issue remaining are related to the interpretation of the physiological results. The authors should either show expression of ASIC3 in CSF-cNs by ISH or tone down their conclusions relative to the ASIC3 receptor in response to pH.

Similarly the authors should be more careful for stating the role PKD2L1 in pH sensing or mechanoreception as they lack genetic methods to investigate its function in lamprey.

A) On the role of ASIC3 in pH sensing :

As stated in my first review, ASIC3 can only be found in the genome of mammals. Only ASIC1 has been identified in the lamprey genome (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3047259) and the lamprey ASIC1a is proton insensitive (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1464184/).

The authors did not respond to my request of testing the role of ASIC1a in their experiments using pharmacology.

One can therefore wonder whether 2uM APETx2 used by the authors (about 100X the EC50 of ASIC3 homomers, https://pubmed.ncbi.nlm.nih.gov/17113616/) could be acting on other targets (see suggested actions on Na or K channels mentioned here, https://pubmed.ncbi.nlm.nih.gov/17113616/). Please discussion clearly in the text.

B) On the role of PKD2L1 in mechanoreception:

The authors summarize that PKD2L1 is responsible for mechanoreception only in dopaminergic CSF-cNs.

However:

1. PKD2L1 is expressed in all CSF-cNs, ventral (dopaminergic) and dorsal (somatostatinergic) as shown by the authors here and found in zebrafish (Djenoune 2014) and mouse (Huang 2006; Petracca 2018). In fact, in their 2016b publication, the authors had proposed that SST+ CSF-cNs were responding to basic pH via PKD2L1, suggesting a role for this channel in these cells.

Why therefore in this study, only mentioning the role of PKD2L1 for mechanoreception in the dopaminergic CSF-cNs despite the expression being there in both dorsal and ventral cell types?

2. Due to the lack of specific antagonists, the authors do not have the tools in lamprey to measure its contribution to mechanoreception. They can only suggest that PKD2L1 contribute to mechanoreception in both ventral and dorsal CSF-cN types.

3. Please correct an error in citation and references used:

The authors use Bohm et al. 2016 to state that CSF-cNs rely on PKD2L1 to be mechanosensory in zebrafish.

To be correct, we showed in vivo in Bohm 2016 that both ventral and dorsal CSF-cNs respond to concave (not convex) mechanical deformations of the spinal cord via PKD2L1 (response are abolished in the KO).

However, mechanoreception cannot be rigorously demonstrated in vivo. It is therefore only in Sternberg 2018 that we could show in vitro using a piezo device to mechanically stimulate their membrane that all CSF-cNs isolated in primary cultures are mechanosensory cells and that their response always rely on PKD2L1.

Note that in zebrafish, we now understand that CSF-cNs in vitro do not respond to CSF flow (Prendergast et al. under review) and in vivo, their response to concave mechanical bending of the spinal cord needs their interaction with the Reissner fiber (Orts Dell Immagine 2020), which does not alter itself the flow (Cantaut-Belarif 2018).

The authors should at minima cite Sternberg et al. 2018 for showing the role of PKD2L1 in mechanoreception, but to be fair, also propose with more nuances in the discussion how these cells in lamprey can combine ASIC and PKD channels to sense pH and mechanical inputs, citing Bohm 2016, Sternberg 2018 and Orts Del Immagine 2020.

Essential revisions:

1. The introduction should give more insights into the underlying biology and also clarify which questions are being asked here: what is new and why these are important, to help the reader understand the biology that underlies this work.

We have rewritten part of the Introduction to better expose the underlying biological questions.

2. The interpretation of physiological results based on established knowledge of ASICs in lamprey cannot be easily explained, and the authors need to provide further data to support their claims or rephrase their conclusions with great care. Specific details follow in the individual reviews below. Electrophysiological recordings of both SST+ and DA+ neurons is important to make conclusions related to the functional roles of these cells, as the microscopy alone cannot infer on the sensory function; alternatively the text needs to be modified in line with suitable conclusions from the morphological analyses.

We have previously reported on the sensitivity of the somatostatin-CSF-contacting neurons (SST+) specifically showing that they respond to both fluid movements and pH changes by electrophysiology (Jalalvand et al. 2016, Nature Communications, Jalalvand et al. 2016, current Biology). We now performed similar experiment on dopaminergic CSF-c neurons in the current study. Since the responses of SST+CSF neurons have been quantified in considerable detail earlier, we feel that there would be no reason to repeat these results in the context of these experiments.

3. Many of the figures were provided in suboptimal resolution and it is essential to be sure they are provided at the appropriate quality.

We provide Figures at high resolution.

Reviewer #1 (Recommendations for the authors):

The work is generally well presented. However, some additional information could make it easier for the non-expert reader. Here are some suggestions:

• A model at the end to compile all results on pH sensitivity, mechanosensing, etc… would greatly simplify the understanding of the messages of the paper.

A new figure, Figure 8, has been added to summarise our findings.

• TH staining is not explained.

Dopaminergic CSF-c neurons express Tyrosine hydroxylase (TH). TH catalyzes the hydroxylation of the Tyrosine to L-DOPA (precursor of Dopamine). Anti-TH antibody has been used to detect TH and is as a marker of dopaminergic neurons. In the Method/Immunohistochemistry section we have explained the type of used TH antibodies and the method of the staining.

• The authors claim a resolution below 10 nm however the size of the antibodies used for staining should be considered.

The 10 nm value was derived by the response of our STED system to point-like objects, such as single antibodies, which are imaged with a FWHM of ~35 10 nm, and further divided by the expansion factor of 4. However, this value refers to how good the response is, which is only one metric to define spatial resolution. When considering biological structures, we agree that the size of the labels indeed matters and influences the resulting ability of resolving and visualizing fine structures. In this work, to be able to tell apart the duplets, we needed at least a resolution of 10-15 nm, which was achieved with ExSTED but not with only STED.

• The authors say "Somatostatin-expressing CSF-c neurons are found throughout the whole volume and located laterally relative to the central canal, while the dopaminergic CSF-c neurons are located ventrally" could the author point that out in figure 1 as I have a hard time visualizing it.

We added in Figure 1 the annotation for dorsal and ventral to guide the viewer to the geometry of the central canal.

• Figure 1f = Cell number per volume? Or total cell number?

We have counted somatostatin and dopamine CSF-c cells in volume of 1.8 × 10⁷ µm³ in each part of the spinal cord. What we see in the graph shows the mean number of the respected cells per volume of each part. We have counted a total of 224 cells in 4 distinct parts of the spinal cord within a volume of 7.2 × 10⁷ µm³. (Information added in figure 1 legend)

• Figure 3g = vesicles per volume? Or total amount of vesicles per soma? Additionally, the increase of number of vesicles in axon is not quantified?

We have clarified the quantification presented in figure 3g in the figure legend. The somatostatin DCVs number density (µm^-2) is reported per cell soma in N = 13 cells. These are 2DSTED images with a pixel size of 20-25 nm and an axial resolution of ~500 nm. (Information added in the Figure legend). We have additionally applied volumetric imaging with 2D STED and quantified somatostatin and dopamine DCVs densities as the number N within the soma volume (N x µm^-3) (added in Figure 3—figure supplement 1 and Figure 4—figure supplement 1). Quantification of the density of vesicles in axons have not been included since we found it hard to have a comprehensive view of the entire axonal network.

• Figure 4d = same comment as for figure 3.

As above we quantified vesicle density in soma areas (N x µm^-2) (2DSTED), and quantification vesicle number density of dopamine DCVs in volume (N x µm^-3) has been added in Figure 4—figure supplement 1 (Information added in the Figure legend).

• Figure 5 = I am wondering why the expansion factor was calculated by measuring the gel slice rather than looking at a sub-cellular structure. Could the authors calculate the expansion factor by measuring cilia diameter in STED and expanded confocal samples?

We agree that a direct check in the sample structure is more sensitive to local change in expansion as well as isotropicity compared to the overall gel especially for the study that precise amount of expansion factor is important. However, to measure the diameter of the same cilia before and after expansion is almost impossible regarding the numerous and packed cilia in the spinal cord samples. In addition to calculating the expansion factor by measuring the size of the sample/gel before and after expansion, we measured the diameter of several cilia (before and after expansion) and calculated the expansion factor by using the means of the diameter from STED and ExSTED. We also made a graph for comparison of cilia diameters (divided by the expansion factor in the expanded one) in the different techniques (the graph added in the Figure 5—figure supplement 2).

Reviewer #2 (Recommendations for the authors):

1. On the microscopy section, the authors make a statement that DSVs within somatostatin-positive sensory cells are present in the axons at baseline and become more numerous upon pH variations. To illustrate their point, the authors need to show :

a) Images illustrating that DSVs were found in the axonal extension (page 5, "data not shown") at baseline, and;

Not visible in Figure 2 but in Figure 3 D-F.

b) Evidence determining whether DSVs are released or transported from the soma to the axon (Figure 3d-f) upon changes of pH. A quantification of the total number of DSV per cell could be insightful to resolve these 2 interpretations.

This point is important for interpretation of the mechanisms at play upon variations of pH.

We quantified the number density of vesicles per soma area (µm^-2) (2DSTED) for somatostatin and dopamine DCVs (explained above). We have additionally applied volumetric imaging and quantified the number density of somatostatin and dopamine DCVs within soma volumes (µm^-3) added in Figure 3—figure supplement 1 and figure 4—figure supplement 1.

2. The interpretation of physiological results based on established knowledge of ASICs in lamprey cannot be easily explained, and the authors need to provide further data to support their claims or rephrase their conclusions:

a) the authors need to show that somatostatin-positive cells respond to pH in their conditions as it is key for their interpretation, and their previous article on all spinal CSF-contacting neurons did not mention that only half the ciliated neurons contacting the CSF responded to pH (Jalalvand et al. Current Biology 2016).

The study of Jalalvand et al. 2016 dealt with one specific subtype of CSF-contacting cells that express somatostatin and are located at the lateral aspect of the central canal and has axonal projections to the lateral margin of the spinal cord (see also new Figure 8). It was known from previous studies that another subtype of CSF-contacting neurons in lamprey expressed dopamine (Schotland et al., 1996), in which the cells have a more ventral location. The latter with a previously unknown function has now been included in the present study and a function unravelled.

b) Similarly, the authors here examine the mechanosensory response of dopaminergic CSF-contacting neurons without quantifying the response of somatostatin-positive neurons to the same stimulus. Both responses to variations of pH and mechanical stimulation need to be performed in both cell types.

We have previously reported on the sensitivity of the somatostatin-CSF-contacting neurons (Jalalvand et al. 2016, Nature Communications) specifically showing that they respond to both fluid movements and pH changes, and that both responses are blocked by APETx2, a specific ASIC3 antagonist. Since these responses have been quantified in considerable detail earlier, we feel that there would be no reason to repeat these results in the context of these experiments.

c) Another group have shown that lamprey only express one type of ASIC channel, ASIC1 (not the channel ASIC3 ): see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3047259/). Therefore, how can the authors explain the effect of the drug used against ASIC3 in this context ?

We note that in the article of Coric et al. 2005, they had identified one clone of cDNA that corresponded to ASIC1 and when expressed in oocytes, no pH sensitivity was found. They do not comment regarding the possible presence of ASIC3. The review of Grunder and Chen (2010) has a focus on ASIC1a and base their comment on lamprey on Coric et al. 2005. Our evidence for the presence of ASIC 3 in lamprey is that both the mechanical and pH response are blocked by APETx2 (Jalalvand et al., 2016). ASIC3 is present in both the peripheral and central nervous system in mammals.

In order to solve this issue, the authors could show expression of ASIC channels, and test the effect of a drug against ASIC1 on the response of somatostatin-expressing cells.

d) Finally, the only ASIC channel expressed in lamprey, ASIC1, when cloned and tested, has been reported to be proton insensitive (see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1464184/) – how then to explain then the activation of a subset of ciliated neurons exposed to protons?

It is correct (Coric et al. 2005) that the ASIC1 clone extracted from lamprey was not pH sensitive when expressed in frog oocytes, but our data show that the mechanical/pH response in the lamprey somatostatin expressing CSF cells in lamprey is mediated by ASIC3, and there is no evidence to suggest that ASIC3 should not be present in lamprey. The dopamine-containing cells are not pH-sensitive.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

[…] Specific comments from one reviewer:

The authors satisfactorily answered some of the issues raised on the quantification of images and provided high resolution images for their outstanding data acquired at high resolution. These data are very convincing and beautiful. Congratulations, this will be very precious and inspirational for further studies in the spinal field !

The issue remaining are related to the interpretation of the physiological results. The authors should either show expression of ASIC3 in CSF-cNs by ISH or tone down their conclusions relative to the ASIC3 receptor in response to pH.

We have reworded our text on page 8 and 13.

Similarly the authors should be more careful for stating the role PKD2L1 in pH sensing or mechanoreception as they lack genetic methods to investigate its function in lamprey.

Since the mechanosensitivity is mediated by an APETx2 sensitive mechanism in somatostatin CSF-c neurons but through another mechanism in the dopamine CSF-c neurons it is natural to point out the possibility of PKDL1, which is responsive in the zebrafish.

The text on page 8 now reads:

“As PKD2L1 has been confirmed as a mechanosensitive ion channel in zebrafish (Bohm et al., 2016, Sternberg et al., 2018, Orts-Del'Immagine et al., 2020), the results suggest that the mechano-sensitivity of the dopaminergic CSF-c neurons may be mediated by PKD2L1 channels.”

A) On the role of ASIC3 in pH sensing :

As stated in my first review, ASIC3 can only be found in the genome of mammals. Only ASIC1 has been identified in the lamprey genome (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3047259) and the lamprey ASIC1a is proton insensitive (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1464184/).

The authors did not respond to my request of testing the role of ASIC1a in their experiments using pharmacology.

We have not tested ASIC1a blockers, since this channel in lamprey is known to not respond to pH (Coric et al. 2005), and ASIC1a is not blocked by APETx2.

One can therefore wonder whether 2uM APETx2 used by the authors (about 100X the EC50 of ASIC3 homomers, https://pubmed-ncbi-nlm-nih-gov/17113616/) could be acting on other targets (see suggested actions on Na or K channels mentioned here, https://pubmed-ncbi-nlm-nih-gov/17113616/). Please discuss clearly in the text.

We work within the specific dose range as described by Diochot et al. 2004, and the same range as has been used in mammalian dorsal root ganglion cells. We also note that APETx2 blocks (the ASIC3 blocker) the mechanosensitive response in somatostatin cell, whereas it has no effect on the mechanosensitivity of dopamine CSF-c cells. This strongly suggests that the effect of APETx2 is not a general unspecific effect on lamprey neurons.

We have added (the following text in the Discussion on ine page 13):

“The mechano-sensitivity of the dopamine and somatostatin expressing CSF-c neurons are mediated by different cellular mechanisms, the latter is blocked by APETx2, a selective ASIC3 blocker as shown in previous studies (E. Jalalvand, B. Robertson, H. Tostivint, et al., 2016; E. Jalalvand, B. Robertson, P. Wallen, et al., 2016). et al. et al. […] The data supports the previous interpretation that ASIC3 mediates both the acid-sensing and the mechanosensitivity in these CSF-c neurons.”

We also responded previously to this issue in our response:

In the article of Coric et al. 2005 they had identified one clone of cDNA that corresponded to ASIC1 and when expressed in oocytes, no pH sensitivity was found under these conditions. They do not comment regarding the possible presence of ASIC3. The review of Grunder and Chen (2010) has a focus on ASIC1a and base their comment in lamprey on Coric et al. 2005. Our evidence for the presence of ASIC3 in lamprey is that both the mechanical and pH response are blocked by APETx2, a selective antagonist of ASIC3, (Jalalvand et al. 2016, Nature. Com), strongly suggesting the presence of ASIC3 in the lamprey. ASIC3 is present in both the peripheral and central nervous system in mammals.

B) On the role of PKD2L1 in mechanoreception:

The authors summarize that PKD2L1 is responsible for mechanoreception only in dopaminergic CSF-cNs.

However:

1. PKD2L1 is expressed in all CSF-cNs, ventral (dopaminergic) and dorsal (somatostatinergic) as shown by the authors here and found in zebrafish (Djenoune 2014) and mouse (Huang 2006; Petracca 2018). In fact, in their 2016b publication, the authors had proposed that SST+ CSF-cNs were responding to basic pH via PKD2L1, suggesting a role for this channel in these cells.

Why therefore in this study, only mentioning the role of PKD2L1 for mechanoreception in the dopaminergic CSF-cNs despite the expression being there in both dorsal and ventral cell types?

Our results clearly show that there are different cellular mechanisms for the mechanotransduction, one blocked by APETx2 in somatostatin CSF-c cells and not in dopamine CSF-c neurons. Since PKD2L1 is present in the dopamine cells and known to be mediate mechanosensitivity in zebrafish CSF-c cells it is natural to point to this possibility.

2. Due to the lack of specific antagonists, the authors do not have the tools in lamprey to measure its contribution to mechanoreception. They can only suggest that PKD2L1 contribute to mechanoreception in both ventral and dorsal CSF-cN types.

The mechanosensitive mechanisms in the two types of CSF-c cells, the laterally projecting and ventral CSF-c neurons, in lamprey must be different, given the difference in APETx2 effects.

3. Please correct an error in citation and references used:

The authors use Bohm et al. 2016 to state that CSF-cNs rely on PKD2L1 to be mechanosensory in zebrafish.

To be correct, we showed in vivo in Bohm 2016 that both ventral and dorsal CSF-cNs respond to concave (not convex) mechanical deformations of the spinal cord via PKD2L1 (response are abolished in the KO).

However, mechanoreception cannot be rigorously demonstrated in vivo. It is therefore only in Sternberg 2018 that we could show in vitro using a piezo device to mechanically stimulate their membrane that all CSF-cNs isolated in primary cultures are mechanosensory cells and that their response always rely on PKD2L1.

Note that in zebrafish, we now understand that CSF-cNs in vitro do not respond to CSF flow (Prendergast et al. under review) and in vivo, their response to concave mechanical bending of the spinal cord needs their interaction with the Reissner fiber (Orts Dell Immagine 2020), which does not alter itself the flow (Cantaut-Belarif 2018).

Interesting with the results with Reissner´s fiber – a possibility that has been considered often but without substantial evidence previously, but not citable as yet.

The authors should at minima cite Sternberg et al. 2018 for showing the role of PKD2L1 in mechanoreception, but to be fair, also propose with more nuances in the discussion how these cells in lamprey can combine ASIC and PKD channels to sense pH and mechanical inputs, citing Bohm 2016, Sternberg 2018 and Orts Del Immagine 2020.

Done.

Author details

1. Elham Jalalvand

   Department of Applied Physics and Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4759-3301

2. Jonatan Alvelid

   Department of Applied Physics and Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden

   Contribution

   Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3554-9322

3. Giovanna Coceano

   Department of Applied Physics and Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden

   Contribution

   Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

4. Steven Edwards

   Department of Applied Physics and Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden

   Contribution

   Formal analysis, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

5. Brita Robertson

   Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden

   Contribution

   Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

6. Sten Grillner

   Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8951-3691

7. Ilaria Testa

   Department of Applied Physics and Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden

   Contribution

   Methodology, Supervision, Validation, Visualization, Writing - original draft, Writing - review and editing

   For correspondence

   ilaria.testa@scilifelab.se

   Competing interests

   Reviewing Editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0003-4005-4997

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