NMJ-Analyser: High-Throughput Morphological Screening of Neuromuscular Junctions

bioRxiv preprint doi: https://doi.org/10.1101/2020.09.24.293886; this version posted September 25, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

1 NMJ-Analyser: high-throughput morphological screening of neuromuscular junctions

2 identifies subtle changes in mouse neuromuscular disease models

4 Alan Mejia Maza1, Seth Jarvis1, Weaverly Colleen Lee1, Thomas J. Cunningham2, Giampietro

5 Schiavo1,3, Maria Secrier4, Pietro Fratta1, James N. Sleigh1,3, Carole H. Sudre5,6,7,* & Elizabeth

6 M.C. Fisher1

8 1 Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology,

9 University College London, London WC1N 3BG, UK.

10 2 Mammalian Genetics Unit, MRC Harwell Institute, Oxfordshire, OX11 0RD, UK.

11 3 UK Dementia Research Institute, University College London, London WC1E 6BT, UK.

12 4 Department of Genetics, Evolution and Environment, UCL Genetic Institute, University

13 College London, London WC1E 6BT, UK.

14 5 MRC Unit for Lifelong Health and Ageing, Department of Population Science and

15 Experimental Medicine, University College London, London WC1E 6BT, UK.

16 6 Centre for Medical Image Computing, Department of Computer Science, University College

17 London, London WC1E 6BT, UK.

18 7 School of Biomedical Engineering and Imaging Sciences, King's College London, London

19 W2CR 2LS, UK.

20 * Corresponding author. E-mail: [email protected]

29 Abstract

30 The neuromuscular junction (NMJ) is the peripheral synapse formed between a motor neuron

31 axon terminal and a muscle fibre. NMJs are thought to be the primary site of peripheral

32 pathology in many neuromuscular diseases, but innervation/denervation status is often

33 assessed qualitatively with poor systematic criteria across studies, and separately from 3D

34 morphological structure. Here, we describe the development of ‘NMJ-Analyser’, to

35 comprehensively screen the morphology of NMJs and their corresponding innervation status

36 automatically. NMJ-Analyser generates 29 biologically relevant features to quantitatively

37 define healthy and aberrant neuromuscular synapses and applies machine learning to

38 diagnose NMJ degeneration. We validated this framework in longitudinal analyses of wildtype

39 mice, as well as in four different neuromuscular disease models: three for amyotrophic lateral

40 sclerosis (ALS) and one for peripheral neuropathy. We showed that structural changes at the

41 NMJ initially occur in the nerve terminal of mutant TDP43 and FUS ALS models. Using a

42 machine learning algorithm, healthy and aberrant neuromuscular synapses are identified with

43 95% accuracy, with 88% sensitivity and 97% specificity. Our results validate NMJ-Analyser as

44 a robust platform for systematic and structural screening of NMJs, and pave the way for

45 transferrable, and cross-comparison and high-throughput studies in neuromuscular diseases.

57 Introduction

58 The neuromuscular junction (NMJ) is the peripheral synapse formed by the (a) nerve terminal

59 of a motor neuron, (b) motor endplate of a muscle fibre and (c) ensheathing terminal Schwann

60 cells 1. In health, NMJs are dynamic structures with constant remodelling, and continuous

61 innervation/denervation 1,2. A permanent morphological change in the NMJ may precede an

62 irreversible denervation process, thought to be the earliest sign of degeneration in multiple

63 human neuromuscular disorders, as well as part of the natural aging process 3–12.

65 Amyotrophic lateral sclerosis (ALS) and Charcot-Marie-Tooth (CMT) disease are devastating

66 neuromuscular disorders. ALS is characterised by progressive degeneration of motor neurons

67 in the brain and spinal cord, leading to skeletal muscle wasting and ultimately death within 2-

68 5 years post symptom onset 13. Mutations in at least 30 genes, including SOD1, FUS, and

69 TARDBP, cause ALS 14–18. CMT is the most common inherited neurological condition,

70 resulting from mutations in >100 genes, and represents a diverse group of neuropathies

71 affecting peripheral motor and sensory nerves 19,20. CMT type 2D (CMT2D) is a subtype

72 caused by mutations in the GARS gene 21.

74 Although NMJ pathology is well-known in mouse models of ALS and CMT2D 6,7,10,22–26, it is

75 unknown whether pre- and post-synapses degenerate synchronously and which are the

76 earliest structural changes. ALS and CMT2D are distinct diseases, but the underlying

77 peripheral pathology in the neuromuscular system can be studied using similar approaches.

78 NMJs in mice are examined routinely by manual or automatic methods 27–29. The manual

79 method entails visual assessment of innervation status (i.e. ‘fully innervated’, ‘partially

80 innervated’ or ‘denervated’), and criteria for NMJ classification can differ, leading to inter-

81 laboratory and inter-rater variabilities, thus limiting further analysis 30–37. Automatic methods

82 include NMJ-morph, an innovative approach based on Image-J software, which has been

83 used to analyse NMJ morphology in different species 38,39. This platform has widespread use

84 because of its efficiency in measuring morphological NMJ features. However, NMJ-morph

85 uses maximum intensity projection images to capture relevant characteristics of en-face

86 NMJs, which may leave behind the great majority of NMJs that acquire complex 3D shapes

87 when wrapping the muscle fibres.

89 Furthermore, a critical aspect of image analysis lies in cross-study comparison of experiments

90 performed at different times. This can be expedited by using the same thresholding process

91 during analyses and acquiring comparable mean fluorescence intensities (MFI) for wildtype

92 samples. However, in current automatic methods, thresholding is performed manually and

93 sometimes relatively arbitrarily (based on staining observed visually), and MFI and innervation

94 status are not considered. Since change in NMJ innervation status is a primary sign of distal

95 dysfunction in multiple neuromuscular diseases, a systematic, bias-free automatic method is

96 urgently needed to accurately and objectively quantify NMJ characteristics, thereby increasing

97 our understanding of cellular and molecular pathogenesis 3,6,40.

99 To address this need, we developed “NMJ-Analyser”, a robust and sensitive automatic method

100 to comprehensively analyse NMJs. NMJ-Analyser enables quantitative assessment of the

101 native 3D conformation of NMJs using an automatic thresholding method, to accurately

102 capture their morphological features. NMJ-Analyser also incorporates a Random Forrest

103 machine learning algorithm to systematically determine the binary characteristics (‘healthy’ or

104 ‘degenerating’) of NMJ innervation status. This is an innovative approach for large-scale,

105 automated, quantifiable and comprehensive NMJ studies. Thus, NMJ-Analyser associates the

106 two most biologically relevant aspects of the neuromuscular synapse: NMJ innervation with a

107 systematic method to capture topological features.

108

109 Here, we describe a comprehensive study of the NMJ using NMJ-Analyser: we evaluated 29

110 biologically relevant morphological parameters and used a Random Forest machine learning

111 algorithm to diagnose NMJ innervation status. We assessed the morphology of mature NMJs

112 in wildtype mice of two different genetic backgrounds and observed high variability in structure

113 of the endplate (i.e. the post-synaptic acetylcholine receptor clusters), whereas nerve

114 terminals appeared conserved. We went on to evaluate three mouse models of ALS

115 (SOD1G93A/+, FUSΔ14/+ and TDP43M323K/M323K) and a CMT2D model (GarsC201R/+) 25,26,41,42. These

116 mice were maintained on different genetic backgrounds, sampled at different ages and

117 disease states, thus giving an ideal opportunity to test NMJ-Analyser. We found NMJ-Analyser

118 was, for the first time, able to detect early structural changes, preceding loss of innervation, in

119 the motor nerve terminals of FUSΔ14/+ and TDP43M323K/ M323K mice. By comparing our method

120 to NMJ-Morph, we show that NMJ-Analyser detects changes at the NMJ with higher

121 sensitivity.

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123 Our results validate NMJ-Analyser as a robust platform for systematic NMJ analysis, and also

124 shed new light on pathology in the assessed mouse models. Our findings show the value of

125 computer-based approaches for reliable and comprehensive analysis of NMJs to identify early

126 changes, and therefore to potentially aid development of therapeutic interventions in patients

127 with neuromuscular diseases.

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141 Results

142 ‘NMJ-Analyser’ for comprehensive and systematic assessment of NMJs in mice. NMJs

143 are dynamic structures with specific age-related morphological features throughout life 1,4,43–

144 45. The morphology of mouse NMJs transitions from poly-innervated (immature, <1-month old

145 mice), mono-innervated (mature, 1 to 18-month old mice) to fragmented/degenerated in aging

146 mice (≥18-month old mice) 43,45,46. NMJ morphological studies have been performed

147 qualitatively or semi-quantitively with different methodologies 6,8,10,24,38,46; results differ,

148 probably due to the muscles studied, age/genetic background of mice and methods used

149 1,4,38,44–47.

150

151 In addition, technical variation and differences in quality of staining across samples contribute

152 to variability (batch effect). Thus, a robust and transferrable platform to study NMJs should

153 ideally incorporate a normalization method for reliable systematic analysis. A strength of NMJ-

154 Analyser lies in its ability to normalize parameters across multiple experiments, making cross-

155 study comparison reliable. For example, z-stack images captured at different resolution can

156 be compared as pixel size input in NMJ-Analyser rather than magnification. Additionally, NMJ-

157 Analyser considers a minimum and maximum size for the pre- or post-synaptic NMJ

158 component, reducing the potential error when quantifying staining. Here, we used the same

159 thresholding cut-off across all experiments leading to uniform capture of staining and reduction

160 of background noise.

161

162 Here, we present NMJ-Analyser, which consists of four steps to comprehensively study NMJs

163 (Fig.1).

164 Step 1: Dissection, staining digitalization and manual assessment of NMJ status. After

165 muscle dissection and staining, NMJ structures are digitalized. NMJ-Analyser requires images

166 in 3D z-stack (3D view) to identify NMJ innervation status. NMJs are classified as fully

167 innervated (‘Full’), partially innervated (‘Partial’) or ‘Denervated’.

168 Step 2: Application of NMJ-Analyser to the stacked raw images and extraction of

169 features. Stacked raw images (plane view) are used to capture the structural features of NMJs

170 using a script we developed in Python. NMJ-Analyser generates twelve biologically relevant

171 parameters for each pre- and post-synaptic structure and five for the interaction between these

172 two NMJ components (29 in total, Table 2). These morphological parameters are non-

173 redundant and biologically relevant.

174 Step 3: Matching manual and automatic assessment. Create a matrix to correlate the

175 qualitative information from step 1 and quantitative data from step 2, for each NMJ, which

176 allows us to correlate the NMJ innervation status to the respective structural features.

177 Step 4: Training with the Random Forest algorithm and evaluation of testing set. Divide

178 the dataset in the matrix into (i) training data (~80% of the data) and (ii) testing data (~20% of

179 the data). The training data are used as an input for a Random Forest machine learning

180 algorithm. The testing data are analysed by the machine learning algorithm to diagnose the

181 innervation status of the NMJs within this dataset. The workflow of NMJ-Analyser can be found

182 in Fig.1 and details about the pipeline are in Additional File 1: Fig.S1.

183

184 Early-mature NMJs display morphological differences compared to mid- and late-

185 mature synaptic structures. Studies of NMJ pathology in small whole-mounted muscles

186 have considerable advantages over analyses in large muscles; such small, thin, flat muscles

187 include lumbricals, FDB and levator auris longus (LAL) 28,48–51. These thin muscles do not need

188 sectioning, reducing the variability in antibody penetration, and permit the entire NMJ

189 innervation/denervation pattern to be accurately assessed 28,48–51. Furthermore, NMJs in

190 regions with different susceptibility to degeneration can be anatomically defined in small

191 muscles 12.

192

193 NMJ architecture acquired at early maturity (mono-innervation, pretzel-like shape, 1 month of

194 age mice) is maintained throughout life 4,45. However, subtle morphological changes may

195 evade detection as most examinations have been qualitative 44,45. Therefore, to quantitively

196 assess such changes, we evaluated biologically relevant NMJ morphological parameters in

197 whole-mounted hindlimb lumbrical muscles in C57BL/6J WT male mice of 1, 3 and 12 months

198 of age (1-month NMJ=early-mature, 3-month NMJ=mid-mature, 12-month NMJ=late-mature;

199 Table 1-2, Fig.2) and C57BL/6J-SJL WT male mice of 1 and 3.5 months of age (Additional

200 File 1: Fig. S2).

201

202 Immunolabeling showed conserved NMJ architecture in C57BL/6J mice of 1, 3 and 12 months

203 of age (Fig.2a). Across these timepoints, NMJs were mono-innervated with a pretzel-like

204 shape indicating maturity (Fig.2a). NMJ-Analyser evaluated twelve morphological features of

205 nerve terminals and endplates, and 5 parameters of the interaction between them in these

206 mice (Fig.2b-c). Overall, matrix correlation plot showed that 90% of parameters were

207 significant correlated (71% positive and 19% negative, Additional File 1: Fig.2b, Spearman

208 correlation). Conversely, mid-mature NMJs displayed fewer (74%) significant positive (45%)

209 and negative (29%) correlations (Fig.2b, Spearman correlation). Late-mature NMJs had even

210 fewer (63%) significant correlations between the parameters (42% positive, 21% negative,

211 Fig.2b, Spearman correlation). These results indicate a higher degree of relationship between

212 morphological parameters of early-mature NMJ than mid- or late-mature neuromuscular

213 synapses of C57BL/6J male mice. Similarly, early-mature NMJs from 1-month old C57BL/6J-

214 SJL mice, showed a greater degree of significant correlation between their morphological

215 parameters than those at 3.5 months of age (75% vs 67%, Additional File 1: Fig.S2, Spearman

216 correlation), but this correlation was weaker compared to the one observed in C57BL/6J male

217 mice.

218

219 Then, we investigated which parameters are preserved or changed during NMJ maturation.

220 Here, morphological parameters were broadly grouped into ‘integrity, ‘shape’, ‘size’ and

221 ‘interaction’ features. ‘integrity referred to the clusterization characteristics of endplate and

222 nerve terminals, ‘shape’ quantified the overall topology, ‘size’ calculated the dimensions of

223 both NMJ components and ‘interaction’ measured the interaction features of pre- and post-

224 synaptic structures (Table 2, Fig.2). Early-mature nerve terminals had the smallest volume,

225 surface and shape factor, but length remained constant across maturity (Fig.2c). Mid- and

226 late-mature nerve terminals were bigger and presented more significant differences from

227 early-mature pre-synaptic NMJs than between themselves (Fig.2c). Motor endplates showed

228 variability across age with no clear pattern observed across maturity (Fig.2c). Morphological

229 parameters measuring the interaction between nerve terminal and endplate showed that mid-

230 and late-mature NMJ structures were conserved and vary significantly from early-mature

231 architecture (Fig.2c). These results were similar in the C57BL/6-SJL mice (Additional File 1:

232 Fig.S2).

233

234 We further explored changes in morphological variables across maturity using PCA (Fig.2d).

235 We observed that nerve terminals of early-, mid- and late-mature NMJs of C57BL/6J mice

236 cluster together while endplate populations were roughly diverging across these timepoints

237 (Fig.2d). Thus, at all ages analysed, endplates presented greater variability between

238 timepoints than nerve terminals.

239

240 Changes in morphological features in degenerating NMJs. Denervation at NMJs is

241 thought to be the earliest sign of peripheral pathology in multiple mouse models of

242 neuromuscular disease 6,52,53. Thus, we determined NMJ innervation status in the hindlimb

243 lumbricals of SOD1G93A/+, FUSΔ14/+, TDP43M323K/M323K and GarsC201R/+ mouse cohorts at the

244 timepoints given (Fig.3a-b). Manual assessment of NMJ innervation status of pre-symptomatic

245 1-, 1.5-month old SOD1G93A/+ and 3-month old FUSΔ14/+ mice showed no significant changes

246 when compared to their WT littermates (Fig.3b). Similarly, mildly affected FUSΔ14/+ and

247 TDP43M323K/M323K mice at 12 months of age had no significant changes in NMJ innervation

248 status compared to WT littermates (Fig.3b). However, we found a significant decrease in the

249 percentage of fully innervated NMJs in 3.5-month old SOD1G93A/+ mice (late symptomatic, 50%

250 vs 97% wildtype controls; Wilcoxon signed-rank test, P-value=0.002, two-tailed), and early

251 symptomatic 1-month old GarsC201R/+ mice (82% vs 100% wildtype controls, Wilcoxon signed-

252 rank test, P-value=0.005, two-tailed) (Fig.3b).

253

254 Using the same NMJs as above, we investigated structural features in SOD1G93A/+ and

255 GarsC201R/+ mice compared to WT littermates (Fig.3c). SOD1G93A/+ mice had the largest

256 changes in NMJ morphological features (i.e. ‘shape’, ‘integrity and ‘size’ of nerve terminal,

257 endplate); interestingly, these changes were more severe in the nerve terminals than the

258 endplates. 1-month old GarsC201R/+ mice showed extensive and significant variation in volume,

259 length, compactness, surface and length of nerve terminal and endplates; the pre- and post-

260 synaptic components were roughly equally affected (Fig.3c).

261

262 A FUS-ALS (Fus ΔNLS/+) 7 and TDP43-ALS (TDP43Q331K/Q331K) 34 showed reduction in the

263 number of motor endplates in the gastrocnemius compared to WT littermates at 1 and 10

264 months, and at 10-12 months of age, respectively. To explore whether similar pathology

265 occurs in our mouse strains, we counted the number of endplates per field in all mutant strains

266 and their WT littermates. We found no significant differences in 1- and 1.5-month old

267 SOD1G93A/+ or 3- and 12-month old FUSΔ14/+ strains. However, while 3.5-month old SOD1G93A/+

268 and 1-month old GarsC201R/+ mice had significant reduction of fully innervated NMJs, motor

269 endplates in these strains were similar to their WT littermates (Fig.3b, Additional File 1:

270 Fig.S3).

271

272 Structural changes in FUSΔ14/+ and TDP43M323K/M323K mice precede NMJ denervation.

273 Subtle structural changes may be an indication of early NMJ degenerative processes 1,3,4.

274 Therefore, we investigated which morphological parameters significantly deviated before NMJ

275 denervation was detectable by eye. We focused on the 12-month old FUSΔ14/+ and

276 TDP43M323K/M323K strains because, although these mice did not have significant changes in

277 NMJ innervation status, we observed significant alteration of ‘non-compactness’ and

278 ‘volume/surface ratio’ in the pre-synaptic component of both strains, but not in motor endplates

279 (Fig.3b-c). Interestingly, ‘coverage’ and ‘intersection, two parameters defining the interaction

280 between nerve terminal and endplate, were significantly reduced in the FUSΔ14/+ and

281 TDP43M323K/M323K strains compared to WT littermates, respectively (Fig.3c). Thus, NMJ-

282 Analyser showed the earliest morphological changes in the pre-synaptic nerve in both strains.

283 In summary, NMJ-Analyser detects early and subtle structural changes in NMJs before the

284 denervation process is detectable by eye.

285

286 Studies on ALS mouse models have shown that muscle fibre depletion occurs in large

287 hindlimb muscles contributing to the pathology 23,54–57. To investigate whether large hindlimb

288 muscles are affected in the FUSΔ14/+ strain, we quantified the fibre type composition in fast-

289 and slow-twitch hindlimb muscles (TA, EDL and soleus, Additional File 1: Fig.S4). Fast-twitch

290 TA and EDL muscles in 3- and 12-month old FUSΔ14/+ mice showed no variation in fibretype

291 composition nor total number of fibres when compared to WT littermates. Similarly, slow-twitch

292 soleus muscle in 3- and 12-month old FUSΔ14/+ mice had no significant differences compared

293 to WT littermates (Fig.S4, Additional File 1: Table S1).

294

295 NMJ-Analyser is more sensitive than NMJ-Morph. NMJ-Morph analyses NMJs that are in

296 an en-face position, which occurs when the endplate is completely extended and faces

297 towards the observer (for examples, see Fig.4a i, ii) 27. The en-face view is ideal to study the

298 size and length of neuromuscular synapses, however, NMJs acquire multiple irregular 3D

299 structures (for example, curved NMJs) as acetylcholine receptors migrate to oppose the motor

300 nerve terminals and the post-synaptic membrane invaginates to enhance neurotransmission

301 efficiency 45. Thus, studying only en-face NMJs may give an incomplete landscape of structural

302 changes.

303

304 Furthermore, NMJ-Morph analyses en-face NMJs using maximum intensity Z-stack

305 projections (2D plane). Studying NMJs in their native 3D conformation may give a more

306 complete picture of the ongoing morphological changes, particularly when structural variations

307 are subtle. NMJ-Analyser tackles these drawbacks by analysing the 3D native structure of

308 neuromuscular synapses regardless of their position in a given muscle.

309

310 We compared methods analysing NMJs in their 3D native structure versus a 2D maximum

311 intensity projection (NMJ-Analyser vs NMJ-Morph, Fig.4), and compared the outputs using a

312 validated commercial software, Volocity, as a reference. Positions of neuromuscular synapses

313 were identified by an independent evaluator in two different batches: 1-month old GarsC201R/+

314 (batch 1, Fig.4) and 1.5-month old SOD1G93A/+ mice (batch 2, Additional File 1: Fig.S5) together

315 with sex-, age-matched WT littermates. These batches were selected as the GarsC201R/+ strain

316 had a mild phenotype and SOD1G93A/+ mice presented no NMJ morphological changes at these

317 ages. From all NMJs observed, 28% and 29% were deemed en-face to the plane of view in

318 batch1 and batch2, respectively. Thus, imaging en-face NMJs may present only a third of the

319 landscape of pathological processes occurring in the lumbrical muscles.

320

321 NMJ-Analyser and NMJ-Morph use different approaches to detect NMJ morphological

322 features (i.e. respectively, single vs multiple thresholding, native 3D vs maximum intensity

323 projection), which may lead to different results. To compare the accuracy of NMJ-Morph and

324 NMJ-Analyser outputs, we used Volocity software as a reference. Volocity is a platform to

325 visualize and quantify morphological structures. Thus, the evaluator randomly selected 61

326 clearly distinguishable en-face NMJs from batch 1 and 49 from batch 2. We compared the

327 correlation of nerve terminal and endplate size obtained by NMJ-Analyser (μm3) or NMJ-

328 Morph (μm2) to Volocity (μm3), as a reference value (Fig.4a). The correlation coefficients of

329 nerve terminal and endplate obtained by NMJ-Analyser were higher compared to NMJ-Morph

330 (R=0.8, P=1.2x10-14 vs R=0.39, P=1.7x10-2 - nerve terminal - and R=0.92 P =2.2x10-16 vs

331 R=0.48, P =9.2x10-5 - endplate -, Fig.4b). Similar results were obtained in batch 2. (Additional

332 File 1: Fig.S5).

333

334 We then split batch 1 into WT and GarsC201R/+ NMJs and found that NMJ-Analyser outputs

335 correlated well to Volocity outputs and were more strongly correlated than those of NMJ-

336 Morph to Volocity (Fig.4c). Similar results were obtained in batch 2. (Additional File 1: Fig.S5).

337 We explored whether NMJ-Analyser, NMJ-Morph and Volocity could detect mild structural

338 variations in neuromuscular synapses by comparing nerve terminal and endplate outputs

339 (Fig.4d-e). Nerve terminal and endplate volumes of GarsC201R/+ showed significant reductions

340 compared to WT littermates, using NMJ-Analyser and Volocity (Fig.4d). However, NMJ-Morph

341 output detected significant decreases only in the endplate area of GarsC201R/+ mice, but not in

342 the nerve terminal (Fig.4d). Similarly, we found that endplate compactness was significantly

343 increased using NMJ-Analyser, but not by NMJ-Morph. These results indicate that NMJ-

344 Analyser replicates the findings of Volocity and detected subtle morphological changes in

345 NMJs, while NMJ-Morph did not detect all of the same differences.

346

347 Machine learning diagnoses healthy and degenerating NMJs. Manual NMJ assessment

348 is performed differently between laboratories, impacting cross-comparison studies on

349 neuromuscular innervation 7,28,30,32,34,35,37,58–60. Manual assessment had limitations when for

350 NMJ metanalysis. Machine learning is an objective technique that can address this limitation;

351 thus, we used the Random Forest (RF) machine learning algorithm to automatically diagnose

352 healthy and degenerating NMJs (Fig.5).

353

354 In our initial observation using PCA, we observed that NMJs in a state of degeneration (i.e.

355 partially, denervated; degenerating) may have some differences from fully innervated

356 synapses (healthy, Fig.5a). Then, we decided to input the morphological features captured to

357 the training RF machine learning algorithm (training dataset). After training step, the RF model

358 diagnosed this binary classification (healthy, degenerating) with 95% accuracy, 88%

359 sensitivity and 97% specificity (Table 3). The area under the curve (AUC), which illustrates the

360 diagnostic ability of our model, was 0.98 (98%, Fig.5b).

361

362 We also identified the most important morphological features as ranked by their permutation

363 importance (i.e. the effect removing them from the dataset has on classification accuracy),

364 along with the actual values of permutation importance and Gini importance. Those features

365 belong to the ‘shape’ and ‘interaction’ of NMJ components (Table 3). While more analysis and

366 data from other independent sources is needed to address any possible biases introduced by

367 our sampling, our results do suggest that morphological features can be used to diagnose the

368 healthy/degenerating state of NMJs.

369

370 Discussion

371 To date, there is no tool for uniform screening of NMJs. Consequently, comparative studies of

372 innervation status and structural analysis are limited, which diminishes our understanding of

373 NMJs in health, ageing and pathology. Here, we describe NMJ-Analyser, a novel platform for

374 morphological screening of NMJs, designed for objective, comparative studies of multiple NMJ

375 structural features. We validated this programme using longitudinal analyses of WT mice,

376 three different genetic ALS mouse models and a CMT2D strain. Using NMJ-Analyser, we have

377 defined changes in WT mice with age and in our pathological models as disease progresses;

378 thus, we have new insights into NMJs in health and in disease, notably including subtle

379 changes in early pathology that may be important for NMJ dysfunction in ALS and CMT2D.

380

381 The topology of the NMJ changes from embryogenesis to maturity 43–45. Recently, Mech et al.,

382 used NMJ-Morph to evaluate five different whole-mounted muscles from 1-month old

383 C57BL/6J WT mice, i.e. early-mature NMJs; muscles analysed were transversus abdominis,

384 flexor digitorum brevis, epitrochleoanconeus and forepaw and hindlimb lumbricals 61. Endplate

385 structures in these early-mature NMJs were more variable between muscles compared to their

386 corresponding axon terminals 61. NMJ coverage in these muscle (percentage of endplate

387 covered by the axon terminal) in the 1-month old WT mice was ~63% and the endplate area

388 was ~2-fold that of nerve terminals 61. Data from NMJ-Analyser is similar: 1-month old WT

389 C57BL/6J mice had 66% coverage and endplate volume was ~1.5-fold that of nerve terminals.

390 Cheng et al., manually assessed en-face TA NMJs in female C57BL/6J WT mice from 2 to 14

391 months of age and reported coverage of ~72%, which was maintained during the period

392 measured, but endplate fragmentation increased with age 47. These results are consistent with

393 our study, in which we found coverage was maintained up to 12 months, but that endplate

394 fragmentation slowly increases with ageing.

395

396 We found no differences in individual NMJ-Analyser parameters for the pre-synaptic

397 components i.e. the axon terminals, in WT mice of 3 and 12 months of age. However, both of

398 these timepoints had significant differences from the pre-synaptic component in mice of 1

399 month of age. Here, as expected, the early-mature pre-synaptic components were significantly

400 smaller than those of mid- (3-month) and late-mature (12-month) NMJs. This could simply be

401 related to age as 3- and 12- month old mice are fully grown while at 1 month they are still

402 developing.

403

404 With respect to individual NMJ-Analyser parameters for the post-synaptic components i.e. the

405 motor endplate, in WT mice we found a different non-linear progression of development. Early-

406 mature endplates (1 month of age) had the lowest value parameters, but mid-mature

407 endplates (3 months of age) had the largest parameter values, slightly above those of late-

408 mature (12 months of age) endplates. Thus, endplates appear to follow an inverted “u” pattern,

409 suggesting that the late-mature post-synapse has a more relaxed structure. This difference

410 between mid-mature and late-mature endplates possibly arises from fragmentation and

411 reduction in AChR cluster numbers, which occurs with ageing in healthy WT mice 47.

412

413 Functional and morphological studies suggest that early decline in muscle function starts at

414 12-months in C57BL/6J WT mice 62. Interestingly, significant reduction in size, orientation,

415 basal calcium content and fission/fusion proteins in mitochondria in the hindlimb FDB muscle

416 were evident in C57BL/6J WT male mice of 10-14 months of age, suggesting incipient muscle

417 decline 63. Similarly, sarcopenia and obvious changes in NMJ architecture are evident in 18-

418 month old healthy WT mice 2,46,64,65. However, motor unit connectivity, grip strength and

419 muscle contractibility do not vary before 18 months of age 66. Hence, it is possible that

420 molecular changes occurring in musculature do not impact muscle function until >18 months

421 of age. More research is needed to clarify why WT nerve terminals appear more stable than

422 endplates, and whether changes within muscle fibres with ageing lead to functional NMJ

423 decline in middle-aged mice (12 months of age).

424

425 NMJ degeneration is a process of sequential structural changes occurring in the nerve

426 terminal and/or endplate 1,67–69. Two fundamental questions in neuromuscular disease remain

427 unsolved: 1) do the pre- or post-synaptic NMJ components degenerate in a synchronised

428 manner in ALS? and 2) does the skeletal muscle drive NMJ destruction? To validate NMJ-

429 Analyser and to gain new insight into neuromuscular degeneration, we assessed individual

430 NMJ features in a set of mouse models for ALS and CMT2D.

431 We carried out manual and automatic NMJ analysis in hindlimb lumbrical muscles of FUSΔ14/+

432 and TDP43M323K/M323K mice at 12 months of age. Both strains expressed the mutant protein at

433 endogenous levels and had mild yet progressive phenotypes, and are thus ideal models for

434 dissecting early-stage disease processes 26,42. We found structural changes in the nerve

435 terminal and the pre- and post-synaptic interaction (for example, ‘compactness’) in both

436 strains, before denervation could be detected by manual analysis, highlighting a major

437 strength of NMJ-Analyser. These results suggest an initial change in neuromuscular

438 morphology, known as remodelling, a process involving the terminal Schwann cells that

439 precedes NMJ dismantling 2,4,5,67,68,70.

440 From our results in the FUSΔ14/+ and TDP43M323K/M323K strains, the axon terminal shows initial

441 pathology. Another FUS-ALS model, ƮONhFUSP525L knock-in mice of 1 month of age presented

442 strong depletion of synaptic vesicles and mitochondria density in the pre-synaptic portion, but

443 mild reduction in the sarcomere length, and no structural changes in the endplate in the TA

444 muscle, although NMJ denervation was observed 30. These findings indicate that initial NMJ

445 remodelling may be associated with alteration of the pre-synaptic morphology and the

446 endplate is affected later.

447 With more advanced disease progression in FUS-ALS and TDP43-ALS strains, post-synaptic

448 changes i.e. motor endplate pathology, becomes evident. For example, heterozygous 1-month

449 old FUSΔNLS/+ knock-in mice, which express a truncated FUS protein at endogenous levels,

450 presented reduction in the area and number of endplates in the TA muscles concomitantly

451 with NMJ denervation 7. Similarly, transgenic TD43M337V/M337V mice, which carry a BAC with the

452 full-length human TARDBP locus and express the mutant protein at lower levels compared to

453 the endogenous gene, have reduced endplate area at 9 months and NMJ denervation at 12

454 months of age in the hindlimb lumbrical muscles 31. The transgenic mice TD43Q331K/Q331K of 10-

455 12-month of age had reduction of motor endplates per section and NMJ denervation 34.

456 The slow progressive phenotype of TDP43M323K/M323K and FUSΔ14/+ mice did not result in NMJ

457 denervation or reduction in endplate numbers, and fibre typing studies in 3- and 12-month old

458 FUSΔ14/+ mice showed no significant changes. More advanced stages of neuromuscular

459 degeneration present with NMJ denervation, endplate loss and sometimes concomitant

460 muscle fibre changes 2,7,31,34,68. Thus, as fibre type remodelling was observed in

461 TD43Q3331K/Q331K (atrophy fibres, regenerating fibres with centralized nuclei), at 10-12 months

462 of age, it is likely that our 12-month TDP43M323K/M323K mice were in an early NMJ remodelling

463 process 34.

464 The SOD1G93A/+ mice have an aggressive phenotype because they overexpress the mutant

465 protein ~24-fold times higher than endogenous gene and thus have a rapid rate of NMJ

466 pathology, which has a preference for fast-twitch muscles 6,23,40,41,53,68,71. We analysed

467 manually the NMJ innervation status in the lumbricals of SOD1G93A/+ mice at 1, 1.5 and 3.5

468 months of age, and the last timepoint showed significant denervation. Our result for the 3.5

469 month timepoint was consistent with previous reports where ~50% or more NMJ denervation

470 was found in fast-twitch muscles (medial gastrocnemius, TA and EDL) 6,22,23,53,72. However,

471 two independent studies found that SOD1G93A/+ mice had either a small (~8%, EDL) or

472 prominent (~50%, gastrocnemius and TA) NMJ denervation at 45 and 47 days of age,

473 respectively. This apparent discrepancy with our results may be explained, in part, by the sex

474 of mice used, muscle fibretype composition, muscle studied and cut-off parameters used for

475 full, partial and denervation NMJ status 6,60. Moreover, the methodology of manual NMJ

476 analysis in mouse models varies across laboratories, and different cut-off parameters for NMJ

477 innervation status limit cross-comparison studies 6,7,36,37,58,59,10,25,26,30,32–35.

478 The GarsC201R/+ mice, which model CMT2D, presented a mild denervation phenotype

479 associated with pre-synaptic and post-synaptic structural NMJ changes at 1 month of age:

480 ~80% of hindpaw lumbrical NMJ were fully innervated, consistent with a recent report 73. At

481 this age, the ‘integrity’ parameter, which measures fragmentation and clusterization of nerve

482 terminals and endplates, was increased on average while parameters quantifying size

483 (volume, length, surface) were significantly decreased compared to WT littermates. This is

484 consistent with a previous report that endplate area of 1- and 3-month old GarsC201R/+ mice

485 was 9% and 12% less than WT, respectively, although this did not reach significance 10. A

486 combination of delayed NMJ development, aberrant interaction between glycyl-tRNA

487 synthetase (GlyRS) and the tropomyosin kinase receptor (TrK) receptors (some of which are

488 found at the NMJ) and differential muscle involvement, all appear to contribute to the

489 GarsC201R/+ mouse phenotype 10,73,74.

490 In summary, we found that subtle quantifiable structural alterations occur primarily in the nerve

491 terminals in 12-month old FUSΔ14/+ and TDP43M323K/M323K strains. We also showed FUSΔ14/+

492 mice had no fibretype switching nor reduction in the numbers of fibres in fast- and slow-twitch

493 muscles at 3 and 12 months of age. Our data suggest that in our FUS and TDP43 ALS

494 physiological models, the pre-synaptic NMJ component degenerates before the endplates and

495 not synchronously. Furthermore, it is unlikely that skeletal muscle drives NMJ destruction as

496 appear to be the case in Spinal-bulbar muscular atrophy disease (SBMA) 75.

497

498 In the transgenic aggressive ALS model SOD1G93A/+, pre-synaptic changes were considerably

499 more pronounced than post-synaptic alterations and we did not see reduction in the number

500 of endplates, at the ages sampled, again, suggesting that pre-synaptic changes precede NMJ

501 pathology and skeletal muscle does not drive NMJ denervation.

502

503 The mild CMT2D model GarsC201R/+ presented similar changes in pre- and post-synaptic NMJ

504 components, indicating a different pathomechanism from ALS models, perhaps due to the

505 primary site of motor neuron pathology (for example, cell body versus distal synapse).

506 NMJ-Analyser showed higher sensitivity than manual NMJ methods and NMJ-Morph, and can

507 quantify NMJs in their native 3D structure independently of their shape acquired in the muscle

508 fibres -- this is particularly important because NMJ topology may change during disease

509 1,4,40,43. These morphometric comparisons require capturing the complete density of three-

510 dimensional shape and interrelations, not only distance or ratio, as needed in two-dimensional

511 images 76–82. Furthermore, image thresholding, multiple validations of the pipeline and

512 coupling to a machine learning algorithm make our programme a robust and sensitive tool for

513 structural studies of neuromuscular synapses.

514

515 NMJ-Analyser is a reliable tool for NMJ topological analysis, but some morphological

516 characteristics were not included in the study. Those parameters include terminal sprouting

517 and poly-innervation and terminal Schwann cell coverage. The code being open-source, any

518 additional contribution of feature is welcome. Another limitation of our study lies in motor

519 endplate quantification: a more precise method is to count the complete number of post-

520 synaptic structures in a given muscle, however, this may technically difficult to achieve.

521

522 Compared to the semi-automated analysis tool in NMJ-Morph, NMJ-Analyser showed greater

523 sensitivity and stronger correlation in detecting morphological NMJ parameters in wildtype and

524 mutant mouse strains. Notably, we were able to diagnose NMJ innervation status confidently

525 using machine learning and demonstrated that NMJ-Analyser is an advancement on the

526 widely-used tool NMJ-Morph. To our knowledge, these are the first data demonstrating that

527 structure and innervation state of NMJs can be studied systematically and in an automatic and

528 integrated manner. Our results validate NMJ-Analyser as a unique and robust platform for

529 systematic analysis of NMJs, and pave the way for transferrable and cross-comparison

530 studies in neuromuscular disease.

531

532 Methods

533 Animals. Three ALS mouse strains (SOD1G93A/+ 41, FUSΔ14/+ 26 and TDP43M323K/M323K 42), a

534 CMT2D mouse model (GarsC201R/+) 25 and their corresponding sex- and age-matched wildtype

535 littermates were used to study NMJ pathology (Table 1). All strains, except the transgenic line

536 SOD1G93A/+, express the mutant protein at physiological levels; details on the genetic

537 background, sex, age and number of mice studied are in Table 1.

538

539 All mouse handling and experiments were performed under license from the United Kingdom

540 Home Office in accordance with the Animals (Scientific Procedures) Act (1986) and approved

541 by the University College London – Queen Square Institute of Neurology Ethics Committee.

542

543 Muscle collection and immunohistochemistry. Lumbrical and flexor digitorum brevis (FDB)

544 muscles located in the hindlimb paws of mice were dissected and stained, as previously

545 described 28,83. Briefly, after dissection, tissues were fixed with 4% paraformaldehyde (PFA,

546 Sigma-Aldrich) for 8-10 min in phosphate buffered saline (PBS) then permeabilized with 2%

547 PBS-Triton X-100 for 30 min to increase antibody penetration. Then, blocking solution (5%

548 donkey serum in 0.05% PBS-Triton X-100) was added to the tissue for 1 h followed by multiple

549 washes of 15-20 min each. Overnight incubations with mouse anti-synaptic vesicle 2 (SV2,

550 1/20, DHSB) and mouse anti-neurofilament medium chain (2H3 1/50, DSHB) in blocking

551 solution at 4ºC were performed to visualise the pre-synaptic NMJ component. After overnight

552 incubation, multiple washes were conducted with PBS-1X for at least 1 h. AlexaFluor-488 anti-

553 mouse IgG secondary antibody (1/2000, Invitrogen) was added together with labelled α-

554 bungarotoxin (to visualise post-synaptic acetylcholine receptors) in blocking solution for 1.5 h

555 at room temperature (RT) (α-btx, Life Technologies). Then, muscles were washed several

556 times and whole-mounted for imaging.

557

558 NMJ imaging. Images of NMJs were obtained using a Zeiss LSM 710 confocal microscope

559 (Zeiss, Germany). All strains were imaged at 512x512 resolution, except for the FUSΔ14/+

560 strain, which was at 1024x1024. Z-stack images were decomposed into individual pre- and

561 post-synaptic planes for posterior analysis (.TIFF, .PNG and .JPEG).

562

563 Manual NMJ analysis. Stacks of images containing complete NMJs were visualised in

564 Volocity software (version 6.5, Perkin Elmer) and their innervation status (fully innervated,

565 partially innervated, denervated) was manually assessed, as previously described 24,28. When

566 a nerve terminal and motor endplate overlapped with at least 50% coverage the NMJ was

567 considered ‘fully innervated’. When between ≥20% and ≤50% overlap occurred, NMJs were

568 considered ‘partially innervated’. Vacant motor endplates were considered ‘denervated’ NMJs.

569 Only NMJs whose innervation status were clearly defined were considered for this study.

570

571 Automatic NMJ analysis. For each manually assessed NMJ, we obtained the 3D position [P

572 (횡, 횢, 횣)] in Euclidean space using Volocity. This step was performed to identify NMJ position

573 and accurately correlate innervation status with the corresponding morphological

574 characteristics (Fig.1, Additional File 1: Fig.S1). In parallel, the stacked images were used as

575 input to the “NMJ-Analyser” framework and to extract their morphological features for nerve

576 terminal, motor endplate and the interaction between them (Fig.1). 29 features were analysed

577 for each NMJ (Table 2). Manually assessed NMJs were then matched to the automatically

578 extracted output using the minimum Euclidean distance between centre of mass. Since Z-

579 stack images frequently contained superimposed NMJs, we only considered NMJs that were

580 clearly differentiated one from another for analysis (Fig.1, manual curation); on average, NMJs

581 need to be separated by at least 20 µm. The final number of NMJs identified by both manual

582 and automatic analysis was 6,728.

583

584 Validation of NMJ-Analyser and manual curation. To confirm the reliability of NMJ-

585 Analyser, we validated key morphological parameters (volume of nerve terminal and endplate)

586 using Volocity software (Additional file 2: Fig.3). Additionally, NMJ-Analyser automatically

587 provides the centre of mass of individual NMJs (3D position, [P (횡, 횢, 횣)]). Centre of mass

588 refers to the average NMJ position in 3D Euclidean space, which is unique for every synapse.

589 The centre of mass position was used to correlate NMJ innervation status identified by Volocity

590 with paired morphological parameters (Fig.1, matrix correlation). We also used ITK SNAP, a

591 3D software viewer, to visually confirm that NMJ-Analyser detected the whole individual NMJ

592 structure and captured its entire 3D morphological structure (Fig.1, manual curation) 84.

593

594 Machine learning. We aimed to reduce the variability in human interpretation by using

595 machine learning. The supervised machine learning technique Random Forest was used to

596 diagnose the innervation status of NMJs (healthy or degenerating) using automatically

597 extracted morphological characteristics. From the total number of NMJs (Additional File 1:

598 Table S2), the dataset was randomly split into a training dataset consisting of 80% of the full

599 dataset and a testing dataset made up of the remaining 20%. While the ratio of degenerating

600 to healthy NMJs was not deliberately preserved, the two datasets were made up of 20% and

601 25% degenerating respectively. We also created an equalised dataset taking 1000 rows at

602 random from degenerating and healthy

603

604 The training and testing models were trained to optimise accuracy. In our testing, prioritising

605 kappa (caret’s other option for classification) did not make a substantial difference to the

606 outcome. The random forest models were trained using caret’s train function, creating one

607 model using the full training dataset and one using the equalised one. Both of these models

608 had 10-fold cross validation. The innervation of the NMJs comprising their respective test

609 datasets were diagnosed and compared to the actual values.

610

611 NMJ-Morph analysis. Semi-quantitative analysis of en-face NMJs was performed as

612 previously described 27. Maximum intensity projections from individual pre- and post-synaptic

613 staining were obtained using the black Zen software (Zeiss, Germany). The Image J manual

614 thresholding method was used as it gave the most consistent results across different methods.

615 Then, binary images were obtained after background elimination. We measured area and

616 length of nerve terminals, and acetylcholine receptor (AChR) area, diameter and area of

617 endplate and compactness.

618

619 Motor endplate counts. Motor endplates of lumbrical muscles were counted in 8-12 fields

620 per mouse. Motor endplates from wildtype and mutant littermates were counted at 40x

621 magnification, except 3- and 12-month old FUSΔ14/+ and their wildtype littermates, which were

622 quantified at 20x magnification.

623

624 Fibre type counts. Tibialis anterior (TA), extensor digitorum longus (EDL) and soleus

625 muscles were snap frozen and transversally sectioned at 20 µm thickness using a microtome

626 (Leica, Germany), and left to dry for 1-2 h. Sections were rehydrated with PBS-1X at RT. Non-

627 specific blocking solution (10% donkey serum in 0.01% PBS-Tween 20) was added to the

628 tissue for 1 h, followed by multiple 15-20 min washes. Overnight incubation consisted of

629 blocking buffer with mouse anti-fibre type I (IgG2b,1/10, DHSB); mouse anti-fibre type IIa

630 (IgG1, 1/20, DHSB), mouse anti-fibre type IIb (IgM, 1/20, DHSB) and rabbit anti-dystrophin

631 (1/750, DHSB), which were used to recognize borders of individual fibres. After overnight

632 incubation and multiple washes with PBS-1X for 1 h, we incubated the sections for 2 h with

633 the following secondary antibodies: anti-mouse IgG2b Alexa Fluor-405, anti-mouse IgG1

634 Alexa Fluor-488, anti-mouse IgM Alexa Fluor-564 and anti-rabbit Alexa Fluor-633. Then,

635 muscle sections were washed several times with PBS and mounted for imaging.

636 Hindlimb sections were imaged at 20x magnification using a Zeiss 7100 Confocal Microscope

637 (Zeiss, Germany). Images were counted using the ‘CellCounter’ plugin on Image J.

638

639 Statistical analysis. Data analysis and plotting were performed using R/RStudio. Schematic

640 graphics found in Fig.1 and Additional File 1: Fig.S1 were created using BioRender. Different

641 plot types were developed to represent various level of analysis. During the study, a single

642 batch experiment included mutants and sex- and age-matched WT littermates, thus, technical

643 variation between genotypes within a batch was assumed null. When comparing multiple

644 batches (i.e. for C57BL/6J and C57BL/6J-SJL male mice, longitudinal study), the following

645 normalization procedure was performed. Normalization protocol can be found at Additional

646 File 2.

647

648 P-value and normality distribution of data. Different approaches to test statistical

649 significance were performed. When comparing NMJ morphological features between

650 genotypes, we tested the normality of data distribution using the Shapiro-Wilk test. In most

651 cases, our data follow a non-normal distribution, thus we conducted non-parametric statistical

652 tests: the Wilcoxon signed-rank test or Kruskal-Wallis test (one-way ANOVA) to compare two

653 or three groups, respectively. P-value adjustment was performed when three or more groups

654 (or variables) were compared simultaneously (Benjamin-Hochberg -BH- post-hoc test).

655

656 When comparing manual NMJ innervation status, motor endplate counts and fibre type

657 counts, we set significance as P-value ≤ 5x10-2, as we compared the percentage or average

658 obtained per mouse (biological replicates). To compare the performance of Volocity, NMJ-

659 Analyser and NMJ-Morph, a P-value ≤ 5x10-2 was considered significant as we used <100

660 NMJs. When we compared individual NMJs, rather than mice, we used a restricted adjusted

661 P-value ≤ 5x10-4 as cut-off for statistical significance.

662

663 Correlation matrix plots were obtained to measure the relationship between two morphological

664 variables. A P-value ≤ 5x10-2 was considered significant. Spearman correlation plots were

665 generated using the corrplot package in R/RStudio.

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825 49. Ling, K. K. Y., Gibbs, R. M., Feng, Z. & Ko, C. P. Severe neuromuscular denervation

826 of clinically relevant muscles in a mouse model of spinal muscular atrophy. Hum. Mol.

827 Genet. (2012). doi:10.1093/hmg/ddr453

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848 57. Lepore, E., Casola, I., Dobrowolny, G. & Musarò, A. Neuromuscular Junction as an

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851 knock-in mouse model of ALS-FTD. Nat. Neurosci. (2018). doi:10.1038/s41593-018-

852 0113-5

853 59. Qiu, H. et al. ALS-associated mutation FUS-R521C causes DNA damage and RNA

854 splicing defects. J. Clin. Invest. (2014). doi:10.1172/JCI72723

855 60. Kalmar, B., Edet-Amana, E. & Greensmith, L. Treatment with a coinducer of the heat

856 shock response delays muscle denervation in the SOD1-G93A mouse model of

857 amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. (2012).

858 doi:10.3109/17482968.2012.660953

859 61. Mech, A. M., Brown, A. L., Schiavo, G. & Sleigh, J. N. Morphological variability is

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862 62. Sayed, R. K. A. et al. Identification of morphological markers of sarcopenia at early

863 stage of aging in skeletal muscle of mice. Exp. Gerontol. (2016).

864 doi:10.1016/j.exger.2016.07.007

865 63. del Campo, A. et al. Muscle function decline and mitochondria changes in middle age

866 precede sarcopenia in mice. Aging (Albany. NY). (2018). doi:10.18632/aging.101358

867 64. Valdez, G., Tapia, J. C., Lichtman, J. W., Fox, M. A. & Sanes, J. R. Shared resistance

868 to aging and als in neuromuscular junctions of specific muscles. PLoS One (2012).

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870 65. Tintignac, L. A., Brenner, H. R. & Rüegg, M. A. Mechanisms regulating

871 neuromuscular junction development and function and causes of muscle wasting.

872 Physiol. Rev. (2015). doi:10.1152/physrev.00033.2014

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878 Sci. Rep. (2016). doi:10.1038/srep24849

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880 Skeletal Muscle in Amyotrophic Lateral Sclerosis. (2016). doi:10.1111/bpa.12350

881 69. Dupuis, L. et al. Muscle mitochondrial uncoupling dismantles neuromuscular junction

882 and triggers distal degeneration of motor neurons. PLoS One (2009).

883 doi:10.1371/journal.pone.0005390

884 70. Deschenes, M. R., Roby, M. A., Eason, M. K. & Harris, M. B. Remodeling of the

885 neuromuscular junction precedes sarcopenia related alterations in myofibers. Exp.

886 Gerontol. (2010). doi:10.1016/j.exger.2010.03.007

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888 copy transgenic mouse, which models human amyotrophic lateral sclerosis. DMM Dis.

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891 model of ALS: Part I, background and methods. Brain Behav. (2013).

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893 73. Sleigh, J. N., Mech, A. M. & Schiavo, G. Developmental demands contribute to early

894 neuromuscular degeneration in CMT2D mice. Cell Death Dis. 11, 564 (2020).

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896 human neuropathy caused by Gars mutations. Proc. Natl. Acad. Sci. U. S. A. (2017).

897 doi:10.1073/pnas.1614557114

898 75. Rinaldi, C., Bott, L. C. & Fischbeck, K. H. Muscle matters in kennedy’s disease.

899 Neuron (2014). doi:10.1016/j.neuron.2014.04.005

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901 morphometrics. Development Genes and Evolution (2016). doi:10.1007/s00427-016-

902 0539-2

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911 neurons in two and three dimensions. J. Neurosci. Methods (1995). doi:10.1016/0165-

912 0270(94)00115-W

913

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915 81. Kim, T. Y., Choi, H. J., Hwang, H. G. & Choi, H. K. Three-dimensional texture analysis

916 of renal cell carcinoma cell nuclei for computerized automatic grading. J. Med. Syst.

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918 82. Kalinin, A. A. et al. 3D Shape Modeling for Cell Nuclear Morphological Analysis and

919 Classification. Sci. Rep. (2018). doi:10.1038/s41598-018-31924-2

920 83. Tarpey, M. D. et al. Characterization and utilization of the flexor digitorum brevis for

921 assessing skeletal muscle function. Skelet. Muscle (2018). doi:10.1186/s13395-018-

922 0160-3

923 84. Yushkevich, P. A. et al. User-guided 3D active contour segmentation of anatomical

924 structures: Significantly improved efficiency and reliability. Neuroimage (2006).

925 doi:10.1016/j.neuroimage.2006.01.015

926 85. Bilsland, L. G. et al. Deficits in axonal transport precede ALS symptoms in vivo. Proc.

927 Natl. Acad. Sci. U. S. A. (2010). doi:10.1073/pnas.1006869107

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942

943 Acknowledgments

944 We are grateful to Prof. Thomas Gillingwater, Prof. Adrian Issacs, Dr Bernadett Kalmar, Dr J.

945 Barney Bryson and Dr Anny Devoy for their helpful suggestions during development of NMJ-

946 Analyser.

947

948 Author Contributions

949 A.M.M. and E.M.C.F. conceived the experiments; A.M.M., S.J, W.C.L., J.N.S., and C.H.S.

950 performed the research; T.J.C. provided tissues samples, A.M.M., S.J. and C.H.S. analysed

951 the data; T.J.C., G.S., M.S., J.N.S, E.M.C.F., P.F. and C.H.S. provided expertise and

952 discussion; A.M.M. and E.M.C.F. wrote the manuscript with inputs from all authors. All authors

953 agreed on the submission of this work.

954

955 Additional Information

956 Supplementary information accompanied this paper can be found at

957 https://github.com/csudre/NMJ_Analyser and

958 https://github.com/SethMagnusJarvis/NMJMachineLearning.

959

960 Competing interests. The authors declare that they have no competing interests.

961

962

963 Table 1 Summary of mutant mice

Strain/MGI Mouse model Background Protein Age Numbers/sex Disease state Ref.964

expression (months) 965

SOD1G93A/+ Transgenic C57BL/6N-SJL ~20-fold 1 5 WT, 5 mutants (male) ALS, pre-symptomatic 41,85 966 MGI:2448770 overexpression

1.5 5 WT, 5 mutants (male) ALS, pre-symptomatic 967 3.5 4 WT, 9 mutants (male) ALS, late symptomatic 968 GarsC201R/+ ENU mutagenesis C57BL/6J Physiological 1 6 WT, 6 mutants (male) CMT2D, early symptomatic 10,25 969 MGI:3760297 expression 3 3 mutants (male) CMT2D, symptomatic 970

FUSΔ14/+ Knock-in, C57BL/6J Physiological 3 4 WT, 4 mutants ALS, pre-symptomatic 26971

MGI:6100933 partial humanization expression (male) 972

973 12 4 WT, 4 mutants ALS, symptomatic

(male) 974

975 TDP43M323K/M32 ENU mutagenesis C57BL/6J- Physiological 12 5 WT, 5 mutants (female) ALS, pre-symptomatic 42 976 3K DBA/2J expression

MGI:6355456 977

978 MGI=Mouse Genome Informatics number

979

980

981 Table 2 Overview of NMJ morphological features

NMJ component Features

Cluster_dist Average distance between fragments or clusters Cluster_size Average size of clusters Integrity Cluster_numbers Number of cluster or fragments Fragmentation Fragmentation index (1-1/Cluster numbers) Nerve terminal/ Non-compactness Measures how compact an NMJ component is Motor Shape factor Numerical description of the shape of an NMJ component endplate Shape Rugosity, internal Number of internal faces, internal irregularity Rugosity, external Number of external faces, external irregularity Length(훍m) Distance between the major-axis endpoints Surface/volume ratio Measures the amount of surface per unit of volume of an NMJ Size component Surface Measures the 3D geometrical uppermost layer of an NMJ component Volume (훍m3) Measures the amount of spaces occupied by an NMJ component Coverage Volume of nerve terminal staining as percentage of endplate Pre- & post- volume Intersection Area of intersecting area between NMJ components, overlapping synaptic Interaction Average dist. Distance between NMJ components Mass-distance Distance between centre of mass Hausdorff distance Maximum distance between the nearest point between NMJ components 982

983

984

985

986

987

988

989

990

991

992

993

994

995

996

997 Table 3 Overview of NMJ diagnosed using machine learning

10-fold RF Morphological features Type Overall Overall permutation Gini

Accuracy 0.9552 Shape factor of endplate Shape 100 47.2

95% CI (0.9417, 0.9664) Shape factor of nerve Shape 58.39 18.6

P-value Intersection Interaction 35.1 100 -16 [Acc > NIR] <2x10

Kappa 0.8604 Internal rugosity of nerves Shape 34.1 31.3

Mcnemar's Test Internal rugosity of endplates Shape 31.5 17.7 (P-value) 0.6774

Sensitivity 0.8809 External rugosity of nerves Shape 27.4 22.88

Specificity 0.9741 coverage Interaction 24.8 40.1

Prevalence 0.2024 External rugosity of nerves Shape 24.1 24.7

Balanced 0.9275 Nerve non-compactness Shape 23.3 43.2 Accuracy Surface/volume ratio of nerves Size 21.1 24.2 998

999

1000

1001

1002

1003

1004

1005

1006

1007

1008

1009

1010

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1013

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1016

1017

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1020

1021

1022

1023 Fig.1 NMJ-Analyser workflow. NMJ-Analyser consists of four sequential steps. (Step 1)

1024 Muscles are dissected, immunostained and digitalised (here, hindlimb lumbrical muscles).

1025 NMJ innervation status is manually assessed (full, partial, denervated) using an imaging

1026 package. (Step 2) Morphological analysis of NMJs requires raw images (plane view) to be

1027 formatted (.TIFF/.JPEG/.PNG). Features are captured for individual pre- or post-synaptic

1028 NMJs and for parameters involving interactions between these two components. (Step 3) Data

1029 from NMJ innervation status and morphological features are integrated into a matrix.

1030 Additional manual curation can be performed to confirm the whole individual NMJ was

1031 captured and its entire 3D structure analysed using NMJ-Analyser. (Step 4) Curated data

1032 output are divided into training and predictive sets. The training set is input into a machine

1033 learning model while the predictive set is used to predict the NMJ innervation status. Additional

1034 File 2 contains a detailed tutorial of how to use NMJ-Analyser.

1035

1036

1037

1038

1039

1040

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1042

1043

1044

1045

1046

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1048

1049

1050

1051

1052

1053

1054 Fig.2 Structural features of healthy NMJs in male C57BL/6J mice. (a) Confocal images of

1055 NMJs visualized using nerve terminal markers (2H3+SV2, green) and motor endplate staining

1056 (alpha bungarotoxin, α-btx, red). Early-, mid- and late- mature NMJs display mono-innervated

1057 axonal input and pretzel-like shape. (b) Matrix correlation plot of nerve terminal and motor

1058 endplate parameters, and interaction between them measured in early-, mid- and late-mature

1059 NMJs. Positive and negative correlations are given by blue and red scales, respectively (P-

1060 value ≤ 5x10-2). Datapoint showed non-normal distribution and spearman correlation tests

1061 were used. Empty circles indicate no significant P-value was observed. (c) Module displaying

1062 NMJ morphological parameters measured in early-, mid- and late-mature NMJs. Early-mature

1063 NMJs are considered as baseline values. Morphological parameters were analysed using

1064 Wilcoxon signed-rank test (d) PCA plots displaying variation between early-, mid- and late-

1065 mature NMJs. Scale bar = 20 μm.

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1067

1068

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1071

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1090

1091 Fig.3 Automatic analysis detects changes before manual assessment of NMJs in ALS

1092 and CMT2D mouse models. (a) Confocal images of NMJs visualized using nerve terminal

1093 markers (2H3+SV2, green) and motor endplate staining (α-btx, red). Images correspond to

1094 SOD1G93A/+ (3.5 months), GarsC201R/+ (1 month), FUS Δ14/+ (12 months) and TDP43M323K/M323K

1095 (12 months). Arrows point to examples of full (dark orange), partial (white) and denervated

1096 NMJs (sky-blue). (b) NMJ innervation status evaluated by eye in whole-mount lumbrical

1097 muscles in ALS and CMT2D mouse models. 3.5-month old SOD1G93A/+ and 1-month old

1098 GarsC201R/+ mice had a significant reduction in the percentage of fully innervated NMJs (* P-

1099 value ≤ 0.05, Wilcoxon signed-rank test, two-tailed) (c) Module displaying NMJ morphological

1100 parameters measured in ALS and CMT2D strains using NMJ-Analyser. Each timepoint

1101 analysed was compared to their respective sex- and aged-matched WT littermates: * P-value

1102 ≤ 5x10-4 was considered significant. Percentage of innervation ± S.E.M. are plotted. Number

1103 of mice, WT and mutant: SOD1G93A/+, 1 month (5+5), 1.5 month (5+5), 3.5 months (4+9);

1104 GarsC201R/+ (5+5); FUSΔ14/+, 3 months (4+4), 12 months (4+4); TDP43M323K/M323K, 12 months

1105 (5+5). Scale bars = 20μm.

1106

1107

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1110

1111

1112

1113

1114

1115

1116

1117

1118

1119

1120

1121

1122

1123 Fig.4 Performance of NMJ-Analyser and NMJ-Morph. (a) Confocal images of (i and ii) en-

1124 face and (iii) curved NMJs, visualized using nerve terminal markers (2H3+SV2, green) and

1125 motor endplate staining (α-btx, red). Images correspond to GarsC201R/+ strain. (b) Correlation

1126 of nerve terminal and endplate output obtained using NMJ-Analyser and NMJ-Morph

1127 compared to a reference software. (c) Correlation of nerve terminal and endplate output

1128 obtained using NMJ-Analyser and NMJ-Morph, divided by genotype. (b-c) Datapoint showed

1129 non-normal distribution and spearman correlation tests were used. Green and red shading

1130 represent the confidence interval. (d) Boxplot of nerve terminal and endplate output using

1131 Volocity, NMJ-Analyser (μm3) and NMJ-Morph (μm2). (e) Boxplot of nerve terminal and

1132 endplate length (μm) and endplate compactness using NMJ-Analyser and NMJ-Morph. c-d

1133 outputs were analysed using a using Wilcoxon signed-rank test. Scale bars = 10μm.

1134

1135

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1164

1165 Fig.5 Machine learning algorithm accurately diagnoses NMJ changing. (a) PCA plot

1166 displaying healthy (fully innervated) and degenerating (partially innervated and denervated

1167 combined) NMJs. (b) AUC plot illustrating the ability of the Random Forest algorithm to

1168 differentiate the between healthy and degenerating NMJs.

1169

1170

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1172

1173

1174

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1193 Additional File 1

1194

1195 Fig.S1 Workflow of image morphological analysis using NMJ-Analyser. Morphological

1196 analysis of NMJs requires (1) pre-processing, (2) processing, (3) measurement and (4) post-

1197 processing of images. (1) Images are required to be in adequate format for identification of

1198 NMJ innervation status and for posterior capture of morphological features (3D and plane

1199 images, respectively). (2) Pre-processing step is used for capturing automatic morphological

1200 features. Images concatenation is used to assigned a 3D position to each NMJ component

1201 (centre of mass, [P (횡, 횢, 횣)]). (3) Processing refers to the cut-off intensity used to define and

1202 differentiate the staining from background (thresholding). It is important to keep the same

1203 threshold between batches for reproducibility. (4) Measurement refers to the capture of

1204 morphological features of pre- and post-synaptic NMJ. (5) Post-processing or manual curation

1205 is required as NMJ-Analyser gives a large output automatically. When analysing images in a

1206 high throughput manner, antibody precipitates or incomplete NMJs may be identified and their

1207 features collected. Thus, using the 3D position of individual objects identified in step 2-4 can

1208 be clearly identified using ITK-SNAP viewer (freely available). These antibody precipitates or

1209 incomplete NMJs identified can be then easily dropped for further analysis.

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1228

1229 Fig.S2 Structural features of healthy NMJs in male C57BL/6J-SJL mice. (a) Confocal

1230 images of NMJs visualized using nerve terminal markers (2H3+SV2, green) and motor

1231 endplate staining (α-btx, red). Early- and mid-mature NMJs display mono-innervated axonal

1232 input and pretzel-like shape. (b) PCA plot showing no major cluster differentiation between

1233 early- and mid-mature NMJs. (c) Module displaying NMJ morphological parameters measured

1234 in early- and mid-mature NMJs. Early-mature NMJs are considered as baseline values.

1235 Morphological parameters were analysed using a using Wilcoxon signed-rank test (d) Matrix

1236 correlation plots of nerve terminal and motor endplate parameters, and interaction between

1237 them measured in early- and mid-mature NMJs. Positive and negative correlations are given

1238 by blue and red scales, respectively (P-value ≤ 5x10-2). Datapoint showed non-normal

1239 distribution and spearman correlation tests were used. Empty circles indicate no significant P

1240 -value was observed. Number of mice, early- and mid-mature: (5+4). Scale bars = 20μm.

1241

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1252

1253 Fig.S3 Motor endplate counts in ALS and CMT2D mouse models. (a) Lumbrical NMJs

1254 from 1.5-month-old WT mice visualized using nerve terminal markers (2H3+SV2, green) and

1255 motor endplate staining (α-btx, red). (b) Bars represent average of motor endplates per field;

1256 8-12 fields per mouse were counted. Number of mice, WT and mutant: SOD1G93A/+, 1-month

1257 (5+5), 1.5-month (5+5), 3.5-months (4+9); GarsC201R/+ (5+5); FUSΔ14/+, 3-months (4+4), 12-

1258 months (4+4); TDP43M323K/M323K, 12-months (5+5). Mean ± S.E.M were plotted. Scale bar =

1259 20μm.

1260

1261

1262

1263

1264

1265

1266

1267

1268

1269 Fig.S4 Fibretype composition in hindlimb muscles of FUSΔ14/+ mice. (a) Transversal

1270 section of TA muscle visualized using fibre markers type I, IIa and IIb. (b) Fibretype

1271 composition of EDL, soleus and TA hindlimb muscles in 3-month old WT and FUSΔ14/+

1272 littermates. (c) High-magnification of TA shown in (a), displaying individual fibretypes.

1273 Fibretype I (blue), IIa (green), IIx (negative staining) and IIb (red). (d) Fibretype composition

1274 of EDL, soleus and TA hindlimb muscles in 12-month old WT and FUSΔ14/+ littermates. Number

1275 of mice, WT and FUSΔ14/+: 3-months (7+8), 12-months (7+8). Male and female mice at each

1276 timepoint (3+4 or 4+4). No significant sex-differences were observed. Mean ± S.E.M were

1277 plotted. Scale bar = 20μm.

1278

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1280

1281

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1307

1308 Fig.S5 Performance of NMJ-Analyser and NMJ-Morph using batch 2. (a) Correlation of

1309 nerve terminal and endplate output obtained using NMJ-Analyser and NMJ-Morph. Datapoint

1310 showed non-normal distribution and spearman correlation tests were used. Green and red

1311 shading represent the confidence interval. (c) Correlation of nerve terminal and endplate

1312 output obtained using NMJ-Analyser and NMJ-Morph, divided by genotype.

1313

1314

1315

1316

1317

1318

1319

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1335

1336 Table S1 Total number of fibres in hindlimb muscles

1337

3-months 12-months

WT FUSΔ14/+ WT FUSΔ14/+

Soleus 861 ± 37 789 ± 66 841 ± 59 832 ± 57

EDL 765 ± 87 837 ± 75 877 ± 58 869 ± 62

TA 2470 ± 208 2654 ± 172 2605 ± 241 2412 ± 250

1338

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1358 Table S2 Total number of NMJs analysed

1359

SOD1 G93A/+ GarsC201R/+ FUSΔ14/+ TDP43M323K/M323k WT

(n=2078) (n=1343) (n=403) (n=95) (2809)

Innervation Fully 1664 340 354 93 2713

status Partial 77 222 24 2 63

Denervated 337 782 25 - 33

Maturity Early 1422 408 - - 1929

Mid 656 935 139 - 440

Late - - 264 95 440

Muscle Lumbricals 2078 408 403 95 2809

FDB - 935 - - -

Phenotype Heathy 1664 340 354 93 2713

Degenerating 414 1003 50 2 96

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1371

1372 Additional File 2

1373

1374 NMJ-Analyser Tutorial

1375 Running NMJ-Analyser

1376 The NMJ-Analyser is a pipeline originally written in Python language.

1377 Step 1.-

1378 - Confirm if Python is installed in your machine.

1379 Mac OSX: type ‘terminal’ in spotlight search. Type ‘python’ to confirm it is installed. A

1380 line command pops up.

1381 Windows: type ‘cmd’ in Start menu. Type ‘python --version’ to confirm it is installed. A

1382 line command pops up.

1383 - If Python is not installed, go to https://www.python.org/downloads/

1384

1385 Step 2.-

1386 To run NMJ-Analyser, need to install the following modules:

1387 - SciPy, for multidimensional image processing: https://www.scipy.org/install.html

1388 - PIL, for reading images: https://pillow.readthedocs.io/en/stable/installation.html

1389 - Glob, for finding the pathnames: https://docs.python.org/3/library/glob.html

1390 - OS: https://pypi.org/project/os-sys/

1391 - Numpy: https://numpy.org/install/

1392 - Argparse: https://pypi.org/project/argparse/

1393 - Pandas: https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html

1394 - Sys: https://docs.python.org/3/install/

1395

1396 Step 3.-

1397 - 3D images should be converted into individual nerve terminal and endplate files (plane

1398 view, .TIFF,.JPEG or .PNG).

1399 - Create a folder ‘MouseTest’ with images file containing the keyword red or RED

1400 (endplate staining), or green or GREEN (nerve staining). The images should be

1401 ordered numerically (i.e. Mouse1_GREEN_0001.jpg.... Mouse1_GREEN_0010.jpg).

1402 - Identify the directory of ‘Test’ folder and Processing.py file.

1403 /Users/’name_user’/…/folder…

1404 - Alternatively, download the ‘MouseTest’ folder (sample images for training) from

1405 https://github.com/csudre/NMJ_Analyser.

1406 -

1407 Step 4.-

1408 - Download NMJ-Analyser scripts from https://github.com/csudre/NMJ_Analyser. Find

1409 the ProcessingGARS.py file.

1410 - Set up the directory (folder where you store the working files) and path files.

1411 - Line 45 in the ProcessingGARS.py file: the plane of resolution (size of pixel, in yellow)

1412 and slice thickness (in red).

1413 - Line 45 in the ProcessingGARS.py file: minimum size of signal to be considered as

1414 positive (min_size, in blue) 1415 def study_component(label, red_filled, red, green, pixdim=[0.38, 0.38,

1416 1.750], min_size=30): 1417

1418 - Line 215: default threshold is established as 64.

1419

1420 Step 6.-

1421 - In the terminal, type the following: (in yellow =pixel size, in red=slice thickness)

1422 - python /’your directory’ /NMJ_Analyser-master/ProcessingGARS.py -p /’your directory’

1423 /MouseTest -dx 0.277 -dz 1.5.

1424 - A .csv file containing a number of parameters will pop. Morphological features for each

1425 nerve terminal (green) and endplate (red).

1426

1427 Normalization

1428 Batch effects are non-biological variation between experiments performed at different

1429 timepoints [48,49]. Variables contributing to batch effect (for example, fixation,

1430 penetration of antibodies, image thresholding and background staining) impact on the

1431 quality and reliability of the immunofluorescence staining. NMJ-Analyser considers the

1432 variability between batches by using the same thresholding protocol for all sections

1433 and using the voxel size as an input to calculate the 3D NMJ features. We considered

1434 a minimum and maximum cut-off size of NMJ structures that are automatically

1435 included in the pipeline (Additional File 1: Fig.S1). In cases where multiple batches

1436 were compared (i.e. for C57BL/6J and C57BL/6J-SJL male mice), the following

1437 normalization protocol was applied: 1) use the same thresholding value and

1438 background correction across batches, 2) compare the mean fluorescence intensity

1439 (MFI) of nerve terminal and endplate in WT samples, 3) use the cumulative distribution

1440 function (ECDF) and Kolmogorov-Smirnov test as a guide of MFI differences across

1441 samples (if not normally distributed), and 4) divide the value of each pre- and post-

1442 synaptic morphological variable by their MFI. Details of the normalization protocol and

1443 R/RStudio scripts can be found at Additional File 1: Fig.S1.

1444 Normalization is required when multiples batches are analysed together. Normalization

1445 procedure assumes the imaging setup was maintained constant, except for the pixel size.

1446

1447 Step 1

1448 - Maintain the same thresholding value and background correction across batches

1449 Step 2

1450 - Only in wildtype samples, identify whether the nerve terminal or endplate MFI follows

1451 normal distribution (Shapiro-Wilk test, a,b).

1452 - Compare mean fluorescence intensity (MFI) of nerve terminal and endplate in WT

1453 samples of each batch. use the cumulative distribution function (ECDF) and

1454 Kolmogorov Smirnov test or Kruskal-Wallis test as a guide of MFI differences across

1455 samples If significantly differ go to step 3 (Fig.S1).

1456

1457 Fig.S1

1458

1459 Step 3

1460 - Divide the value of each pre- and post-synaptic morphological variable by their

1461 corresponding MFI.

1462

1463 Manual curation

1464 Images containing multiple NMJs require manual inspection. This procedure is required to

1465 fully identify individual NMJs and avoid poor counting. Thus, it is possible that two or more

1466 NMJs can be counted as one if they are close (<20μm).

1467

1468

1469 Step1

1470 - Download and install ITK-SNAP viewer: http://www.itksnap.org/pmwiki/pmwiki.php

1471 - Open the confocal image (.lsm5 format) on a ITK-SNAP.

1472 - Identify each NMJ obtained from NMJ-Analyser .csv output by typing the position

1473 (x,y,z) on ‘cursor position’ (Fig.S2).

1474

1475 Fig.S2

1476

1477

1478 Machine Learning

1479 Machine learning pipeline and instructions to run the machine learning platform can be

1480 downloaded from https://github.com/SethMagnusJarvis/NMJMachineLearning

1481

1482

1483 NMJ-Analyser validation

1484 We compared the nerve terminal and endplate volume outputs obtained by of NMJ-Analyser

1485 and Volocity. The figure below shows strong correlation between outputs obtained by NMJ-

1486 Analyser and Volocity (Fig.S3)

1487

1488

1489 Fig.S3

1490

1491 1492