Targeted antisense oligonucleotide treatment rescues developmental alterations in spinal muscular atrophy organoids

Domain: chemistry
Published: 2025-12-21
Editorial outcome: major_revision

Abstract
Spinal muscular atrophy (SMA) is a severe neurological disease caused by mutations in the SMN1 gene, characterized by early onset and degeneration of lower motor neurons. Understanding early neurodevelopmental defects in SMA is crucial for optimizing therapeutic interventions. Using spinal cord and cerebral organoids generated from multiple SMA type 1 male donors, we revealed widespread disease mechanisms beyond motor neuron degeneration. Single-cell transcriptomics uncovered pervasive alterations across neural populations, from progenitors to neurons, demonstrating SMN-dependent dysregulation of neuronal differentiation programs. Multi-electrode array (MEA) analysis identified consistent hyperexcitability in both spinal and brain organoids, establishing altered electrical properties as a central nervous system-wide feature of pathogenesis. Early administration of an optimized antisense oligonucleotide (ASO) that increased SMN levels rescued morphological and functional deficits in spinal cord organoids across different genetic backgrounds. Importantly, this early intervention precisely corrected aberrant splicing in here identified SMN1 targets enriched at critical nodes of neuronal differentiation. Our findings demonstrate that early developmental defects are core features of SMA pathogenesis that can be prevented by timely therapeutic intervention, providing insights for optimizing treatment strategies.

Introduction
Spinal muscular atrophy (SMA) is a severe inherited neurological disorder and still one of the leading genetic causes of death in childhood1,2. SMA is caused by autosomal recessive mutations in SMN1, resulting in reduced expression of full-length SMN protein3. This leads to progressive degeneration of more vulnerable spinal motor neurons (MNs), causing proximal muscle weakness and atrophy, and early life-threatening respiratory failure4,5. SMA phenotypic spectrum is very broad and consists of different clinical forms, from fatal to mild, which are ranked on the base of the motor milestones reached by the patients6.

The SMN protein is expressed in several fetal tissues, including the brain, spinal cord, skeletal muscle, heart, and kidney; it significantly decreases after birth, suggesting that SMN is particularly important during prenatal life7,8. Recent data from human SMA autopsies associated abnormal MN axon development in utero with postnatal degeneration in SMA9. Beyond spinal cord involvement, defects in cognitive function and brain neuropathology have been reported in SMA patients, but only partially documented10. The early events that drive the degeneration as well as their impact on the entire central nervous system (CNS), are still largely unknown: considering pre-symptomatic factors is key when modeling the disease.

Recently, FDA/EMA approved breakthrough therapeutic strategies to restore the levels of SMN and they are currently in clinical use5,11. These approaches are based on either cDNA replacement of SMN1 or the use of antisense oligonucleotides (ASOs) or small molecules to achieve splicing correction of SMN2. SMN2 is a paralog of SMN1 that produces a truncated and rapidly degraded SMN protein, which can be used as a point of action to restore SMN levels12. These drugs have dramatically changed the clinical prognosis of SMA, and for the fatal to severe SMA type 1, are able to reduce the disease to a chronic condition by halting or preventing motor disability. Although growing clinical data show that approved treatments also provide benefits in the chronic phase, it is emerging that the greatest impact is achieved when patients are treated pre-symptomatically5. Remaining unmet therapeutic needs are the treatment of patients after symptom onset5 and the elusive understanding of the factors halting treatment efficacy. Critically, even newborns identified through screening show incomplete motor recovery, particularly those with two SMN2 copies13. Therefore, a better understanding of SMA mechanisms in prenatal disease stages is pivotal to optimizing current therapeutic approaches with the aim of increasing efficiency, targeting biodistribution, and developing new clinical strategies.

Conventional 2D cultures and animal models can only partially recapitulate the phenotypic and genetic complexity of human MN diseases. CNS 3D human organoids14–16, in contrast, display a high degree of cellular diversity, enabling them to model complex cell–cell interactions, while modeling the patient-specific genetic background.

Here, we leveraged an efficient protocol for the generation of induced pluripotent stem cell (iPSC)-derived spinal cord organoids (SCOs) and unveiled early clinically relevant SMA pathological phenotypes. We detected alterations in progenitors’ behaviour, early neuronal differentiation defects, even before hallmarks of cell death appeared, and identified disease susceptibility for distinct neuronal subtypes, beyond MNs.

A pervasive alteration of the splicing machinery has been involved in several neurodegenerative diseases, and it has been recently postulated for the adult MN disease amyotrophic lateral sclerosis (ALS)17. SMN protein has a recognized role in the splicing machinery, but the impact of its disruption on developmental processes remains still unexplored. Using SCOs, we identified genes with abnormal splicing in SMA and those rescued by SMN-targeting therapies. This allowed us to study how restoring SMN reshapes the transcriptional landscape in patient-derived SCOs. Interestingly, we found that SMN deficiency disrupts the splicing of key genes involved in neural differentiation, potentially triggering SMA pathogenesis. Our findings demonstrate SMN critical role in neural development and the significance of its restoration in therapeutic strategies.

Results
SMN deficiency impacts progenitors and neurons in the spinal cord
We derived human SCOs from healthy control (CTRL, n=5) and SMA type 1 (n=5) patient-derived iPSCS lines (Supplementary Fig. 1 and Supplementary Data 1). Spinal cord development is a complex process that entails both rostro-caudal specification, mediated by Wnt and retinoic acid (RA), and dorso-ventral patterning, downstream of sonic hedgehog (SHH) signaling18,19. Thus, SCOs were cultured in the presence of small molecules acting on these pathways to recapitulate in vitro the spinal cord specification20–22 (Fig. 1a). In both CTRL and SMA organoids, the treatment promoted progressive caudal neuralization, as evidenced by the appearance of neuronal progenitors at the early differentiation stage (1 month), marked by the expression of PAX6 and NESTIN (Fig. 1b). This was followed by the appearance of the neuronal markers DCX and MAP2, indicating ongoing neuronal differentiation (Fig. 1b, Supplementary Fig. 2, and Supplementary Data 2 and 3). As the differentiation proceeded, we observed the presence of spinal precursors expressing HOXB4 and OLIG2 along with ISL1- and MNX1-positive MNs (Fig. 1b and Supplementary Fig. 2). At 2 months, we also detected the presence of astroglial cells (Supplementary Fig. 2). Together, the data show that SCOs can recapitulate key aspects of spinal cord neurogenesis.

To explore the early events driving SMA pathogenesis, we analyzed 2-month-old CTRL and SMA SCOs composition by single-cell RNA-seq (C1, C3, S1, and S3 lines, Fig. 1c–e, Supplementary Fig. 3, and Supplementary Data 4). Accounting for technical variability, we sequenced individual SCOs deriving from 2 CTRL and 2 SMA lines (Supplementary Data 1) and successfully profiled a total of 27,114 cells. Module annotation with defined sets of marker genes for dorsal and ventral domains identified different classes of neural progenitors (Prog, SOX2, SOX9), including floor plate progenitors (pFP, SHH), differentiating neurons (Neuron, DCX), and subtypes of spinal cord neuronal domains (Fig. 1c–e and Supplementary Fig. 3). At this stage, we could also identify distinct clusters of MN precursors (pMN, OLIG2) and postmitotic MNs (MN, PHOX2B, ISL1) (Fig. 1c–e, Supplementary Figs. 3d–g and 4, and Supplementary Data 4). The selective expression of defined combinations of HOX transcripts associated with a posterior neural tube patterning suggested the acquisition of a cervical spinal cord identity (Supplementary Fig. 3d). To benchmark CTRL SCOs against human embryonic and fetal spinal cord development, we used two complementary transcriptomic datasets. The first, a single-cell RNA-seq dataset spanning a broad range of developmental stages23, allowed us to identify the time point most closely resembling our organoids, revealing a strong transcriptional similarity with first-trimester spinal cord, peaking at gestational week 8 (Supplementary Fig. 3e). The second dataset24, a high-resolution single-cell atlas of the human fetal spinal cord (4–8 gestational weeks), enabled precise mapping of the cellular composition of SCOs. This analysis showed that SCOs closely matched major neural cell classes, including progenitors and neurons, while lacking similarity with mesodermal or endodermal lineages (Supplementary Fig. 3f, g). Together, these findings support the use of SCOs as a robust in vitro model for studying neurological diseases with multicellular involvement, such as SMA.

To investigate whether cell composition and relative contribution of neuronal types are altered in the absence of SMN protein, we performed a differential abundance test using the MiloPy framework25 (Fig. 1f, g). This revealed that specific cell subtypes were differentially represented in SMA organoids, with MNs significantly decreased in SMA genotype.

We further extended our analyses to include pairwise comparisons between individual genotypes/lines in order to evaluate the consistency of our findings across conditions. Except for the C1 vs. S3 comparison, all line-to-line comparisons revealed a significant reduction in MN numbers in SMA relative to CTRL. Considering the inherent variability of the in vitro system, these results nevertheless demonstrate a significant reduction in MN numbers at this stage (Supplementary Fig. 5). Interestingly, in contrast with the reduction of MNs, pMNs were enriched in SMA organoids, as well as other clusters of neural progenitors (Fig. 1f, g). These results support a broader effect on neural diversity in the reduction of SMN, which impacts the proportion of different cell types in the developing organoids.

We therefore investigated the transcriptional changes associated with the lack of SMN in each cluster. To account for the differences in abundance presented in SMA SCOs, we performed differential gene expression analysis in clusters presenting at least 25% of cells of each genotype (Fig. 1h and Supplementary Fig. 6; absolute log fold change ≥ 0.5 and adjusted p-value < 0.05). While the largest proportion of deregulated genes (2031 genes differentially expressed between CTRL and SMA SCOs, 772 upregulated and 1259 downregulated) was observed in cells ascribed to the MN cluster, progenitor cells were the next most affected (Supplementary Data 5). Interestingly, specific genes related to progenitor fitness were downregulated (ASCL1, SOX4), while CASP9, involved in the apoptosis signaling, was upregulated (Fig. 1i). Gene ontology analysis on progenitors (Fig. 1j) revealed a pervasive effect of the lack of SMN on pathways involved in several developmental processes. Overall, these findings highlight the profound impact of SMN deficiency on the cellular composition and transcriptional landscape of SCOs. While the significant reduction in spinal MNs is a key hallmark of SMA, the altered abundance and transcriptional dysregulation of progenitor cells reveal an equally critical aspect of the disease pathology with a broader disruption in neural development, often disregarded in the investigation of this disease.

SMA progenitor defects precede motor neuron impairment
To longitudinally capture the differentiation progression phenotype at the cellular level, we performed high-content image analysis (Fig. 2a–d). Notably, aberrant differentiation trajectories were observed in SMA SCOs. While CTRL SCOs showed a clear reduction in SOX2-expressing neural progenitors and an increase in the post-mitotic neuronal marker TUJ1 from 1 to 2 months, SMA SCOs exhibited a less pronounced reduction in SOX2 and no increase in the proportion of TUJ1-expressing cells (Fig. 2a–d; percentage of SOX2-positive cells in each organoid at 1 vs. 2 months: CTRL 47.57% ± 11.94 vs. 20.40% ± 13.85, p < 0.0001; SMA 38.79% ± 14.42 vs. 25.44% ± 13.91, p = 0.0148; percentage of TUJ1 positive cells in each organoid at 1 vs. 2 months: CTRL 13.52% ± 4.788 vs. 28.42% ± 12.35, p = 0.0002; SMA 21.49% ± 14.28 vs. 13.84% ± 6.759, p = 0.0764). Accordingly, the SOX2/TUJ1 ratio was significantly higher in the control group at day 30 (p = 0.0444) but showed a marked decrease by day 60, indicating a shift from progenitor cells to differentiated neurons over time, in line with the expected physiological trajectory. However, this pattern was not observed in the SMA condition (Fig. 2e).

Since SCOs recapitulate the early stage of spinal cord development, and our data suggest that the physiological process of cell death could be enhanced in SMA, we longitudinally tested CTRL and SMA SCOs for programmed cell death. SMA SCOs indeed exhibited a difference in apoptosis dynamics in comparison with CTRL SCOs, with a steep enhancement at 2 months (percentage of apoptotic nuclei in each organoid at 1 vs. 2 months: CTRL 11.30% ± 2.512 vs. 7.369% ± 5.125, p = 0.3786; SMA, 4.245% ± 2.372 vs. 23.02% ± 11.34, p < 0.0001; Supplementary Fig. 7a, b). Enrichment analysis of apoptosis-related biological functions, based on differentially expressed genes (DEG) between SMA and CTRL SCOs, revealed that these processes were predominantly upregulated in SMA pMNs and MNs (Supplementary Fig. 7c).

Differences in cell death rate might explain, at least in part, the reduced MN population in SMA SCOs captured by MiloPy (Fig. 1f, g), while the altered cell type composition suggests changes in early developmental programs in SMA tissue.

To examine differences in developmental dynamics from progenitors to neuronal states, we performed pseudotemporal ordering of cells using CytoTRACE, implemented via the CellRank framework (Fig. 2e). This method infers cellular differentiation status based on transcriptional complexity, and importantly, it is agnostic to the choice of root cell, allowing unbiased pseudotemporal ordering without requiring prior assumptions about lineage starting points. In line with in vivo human fetal development24, CTRL organoids displayed a continuous progression from progenitor populations to differentiated neuronal cell types (Fig. 2e, f). In contrast, SMA SCOs displayed a significant global shift toward lower developmental maturity (Kolmogorov–Smirnov (KS) test, p = 2.16e–08) that could also be shown across all genotypes (Supplementary Fig. 9). Cell-type-specific analyses further revealed a robust, statistically significant divergence in the pseudotime score distributions (Fig. 2g).

Altogether, these data support our previous results pointing at an early alteration in SMA neurodevelopment, where cells pause at intermediate developmental states unable to proceed along their maturation, which could, in turn, impact in their ability to survive in the nascent circuits.

SMA motor neuron development trajectory is hindered
To investigate gene expression changes during MN differentiation, we partitioned the dataset to focus specifically on MN lineage, including pMNs, early and mature MNs (Fig. 3 and Supplementary Fig. 8). We identified the main categories of somatic MNs, with little to no evidence of a distinct visceral MN population, as visceral markers were either absent or co-expressed with skeletal MN markers (Supplementary Fig. 8b). Using diffusion mapping of the MN lineage subset, we visualized eight distinct transcriptional clusters (Supplementary Fig. 8a) corresponding to three individual MN subtypes based on the expression levels of PHOX2B vs. ISL1/MNX1 (Supplementary Fig. 8b–e). Despite the overall higher abundance of MN cells in CTRL genotypes (Fig. 1f, g), the MN-1 ISL1+/MNX1+ subpopulation was preferentially enriched in SMA genotypes.

Next, we explored developmental pathways within the MN partition, allowing us to map the neurogenic progression from pMN to mature MNs (Fig. 3a–c). In CTRL SCOs, the neurogenic trajectory from pMNs to MNs followed the expected sequential progression during differentiation (Fig. 3b, c). A different scenario emerged in the MN differentiation trajectory of SMA SCOs. In contrast to the continuous progression seen in CTRL samples, the differentiation trajectory in SMA appeared polarized. This polarization was characterized by a concentration of cells at the progenitor state at the beginning of the trajectory, an accumulation of cells in an intermediate state, and a notable enrichment of MNs at the end of the trajectory, predominantly within a single branch (Fig. 3c). Importantly, consistent results were obtained when the dataset was analyzed by individual cell lines, suggesting that while the relative abundance of MNs differs among lines, the underlying developmental alteration is a shared feature across all (Supplementary Fig. 9). This suggests that SMN deficiency may disrupt the progressive acquisition of fate along MN differentiation, leading to an imbalance between progenitors and terminally differentiated neurons.

The interplay between specific transcription factors (TFs) is pivotal for the execution of correct developmental programs in the spinal cord26; cell-type-specific TFs represent central hubs in gene regulatory networks and alteration of their expression is associated with downstream defects in cell identity acquisition and maintenance18. To investigate whether the early developmental defects associated with SMA disease were driven by changes in TF activity, we used pySCENIC (Single-Cell Regulatory Network Inference and Clustering) algorithm27 as a tool for the selection of key TFs along the developmental trajectory of CTRL and SMA SCOs organoids (Fig. 3d, e). Interestingly, we found several TFs known to control key aspects of the molecular development of the human spinal cord (i.e., SOX2, SOX9, OLIG2, NKX6-1) expressed along the different states of the trajectories in SMA and CTRL SCO (Fig. 3d, e), suggesting important variations in the activity of transcriptional programs among cell states in absence of SMN. Histone modifiers and regulators of chromatin architecture were also identified (CHD2, HDAC2, KDM5A), which could be important determinants in human spinal cord development and could impact SMA disease course.

To determine whether the developmental TF expression implied differential gene regulatory programs, we calculated regulon activity. We observed that shared regulons of TFs key for MN differentiation (e.g., SOX2, PHOX2B, CHD2) had shifted profile of activity in SMA, with a sustained activity of SOX2- transcriptional programs, which is associated with the prolonged maintenance of progenitor states (Fig. 3d, e). Together, these analyses support the idea that, not only SMA SCOs fail to properly differentiate into mature MNs, but also show that the neuronal developmental trajectory, with the associated transcriptional regulations, is strongly altered from the early progenitor state. This might impinge on MN survival as well as influence the overall spinal cord neuronal differentiation.

We then looked at the transcriptional landscape of SMA vs. CTRL MNs (Fig. 3f, g). SMA MNs showed downregulation of genes involved in neuronal growth and synaptic maturation (TUBB3, TUBB, SCN5A). Notably, a small subset of genes showed consistent deregulation in virtually all cell types in SMA SCOs (Supplementary Data 5). They included CHCHD2, a mitochondrial protein involved in oxidative phosphorylation and whose upregulation was previously shown in other neurodegenerative conditions to be serving a compensatory protective response against oxidative stress28,29. Interestingly, CHCHD2 has been shown to be subjected to alternative splicing. We validated by qPCR that this gene reproducibly displayed the same trend of deregulation across individual SCOs derived from two independent lines of CTRL and SMA (Supplementary Fig. 10a), while no difference was observed in multiple lines of undifferentiated SMA iPSCs (Supplementary Fig. 10b). Overall, SMA SCOs exhibit a disease-associated molecular signature across various MN maturation states, leading to global alterations in multiple molecular programs. Gene ontology analysis defined enriched categories related to metabolic processes, stress response and spinal cord disorders (Fig. 3g). Indeed, a significant reduction in neural projections (identified with staining for the heavy neurofilament NEF-H/SMI32, a marker gene enriched in MNs) was detected in SMA neurons derived from dissociated SCOs (CTRL 59.17 ± 12.30 µm vs. SMA 34.62 ± 8.34 µm, p < 0.0001; Supplementary Fig. 8f, g). Finally, we examined molecular programs associated with cell death by calculating module scores for apoptosis in each MN partition in CTRL vs. SMA conditions (Fig. 3h). We found a significant enrichment of cell death–related molecular programs in SMA MNs across all subgroups. This indicates that, although some MN subtypes are more represented in SMA at this time point, they not only exhibit dysregulation of developmental trajectories but have also already initiated degenerative cascades associated with MN loss.

Pathological patterns of activity in SMA spinal cord organoids
Modifications in the electrophysiological activity of spinal MNs have previously been reported in 2D cultures30 and pre-symptomatic mouse models of SMA31. As in other motor degenerative disorders32, this abnormal activity might contribute to the SMA human disease phenotype. However, it is not clear how early in disease progression this dysfunction occurs and how it varies across CNS compartments. We exploited high-density multi-electrode array (HD-MEA) technology to investigate the functionality of SCOs derived from SMA patients (Fig. 4a–c). Basal recordings of 2-month-old CTRL and SMA SCOs showed spontaneous spiking activity, with significantly lower frequencies in SMA SCOs (CTRL 1.956 ± 0.5354 Hz vs. SMA 1.355 ± 0.6807 Hz, p = 0.0127; Fig. 4b, c). Moreover, SMA SCOs significantly increased their firing frequency more than CTRL SCOs after a glutamatergic stimulus, suggesting an early dysfunction with features of hyperexcitability (CTRL 44.06% ± 20.03 vs. SMA 91.03% ± 28.42, p < 0.0001; Fig. 4b, c).

Notably, DEG analyses revealed, across different clusters, the presence of a set of deregulated genes related to synaptic channels among different neuronal populations, suggesting an overall impact on synaptic function (Supplementary Fig. 11a). To rule out changes in GABAergic transmission, we tested spontaneous firing frequency upon application, at the basal level, of gabazine and strychnine33 to block GABA-A and glycine receptors. As expected, an increased spontaneous firing frequency was observed in CTRL organoids (CTRL 44.21% ± 5.626) and a similar behaviour was detected in SMA SCOs (SMA 43.75% ± 16.4, p = 0.9521; Supplementary Fig. 11b–d). This response suggested the presence of active mechanisms of neuronal inhibition in the organoid circuits (CTRL 43.71% ± 36.38 vs. SMA 35.72% ± 19.11, p = 0.5455). GAD65 staining confirmed the presence of inhibitory interneurons in both CTRL and SMA SCOs (Supplementary Fig. 11b lower panel), as also highlighted by single-cell molecular data. These results support the hypothesis that the observed hyperexcitability response of SMA organoids to glutamate does not imply a reduced inhibitory transmission, and it is more likely to be ascribed to alterations in glutamatergic transmission maturation.

Overall, we showed that SCOs display basal firing activity with the potential to respond to an exogenous stimulus and they generate functional circuits. Furthermore, SMA SCOs possess distinct electrophysiological properties, including increased glutamate sensitivity, that can potentially lead to hyperexcitability, a common trait of many neurodegenerative disorders.

Pathological patterns of activity in SMA cerebral organoids
SMA has traditionally been considered a lower MN disease; nonetheless, cerebral involvement has been reported in the most severe SMA cases10. It is still debatable whether this feature is directly linked to SMA pathogenesis, or it is indirectly associated with spinal degeneration retrogradely spreading to the brain. To begin exploring these alternatives, we employed an adapted protocol for generating 3D cerebral organoids14 from SMA and healthy iPSC lines (Fig. 4d, e and Supplementary Fig. 12), where a putative secondary effect of spinal cord degeneration was excluded. Both CTRL and SMA cerebral organoids displayed complex morphology, with heterogeneous regions containing neural progenitors (SOX2+), mainly localized within neural rosettes, and neurons (TUJ1+; Fig. 4f). We quantified the number of SOX2-positive progenitors across CTRL and SMA brain organoids at both the RNA and protein levels and did not detect a significant difference at the 2-month time point (CTRL 57.45 ± 19.10 vs. SMA 67.02 ± 11.54, p = 0.2449; Fig. 4g). In parallel, we did not detect any significant difference for the expression of NEF-H/SMI32 (Supplementary Fig. 12c). Staining for glial populations (GFAP+), cortical neurons (CTIP2+ and SATB2+) and general neuronal markers (DCX and MAP2) at 2 months revealed the presence of a variety of cell types (Supplementary Fig. 12a, b). As for SCOs, brain differentiation can take place from SMA reprogrammed pluripotent cells and cerebral organoids are able to recapitulate key early steps of brain development.

Interestingly, cerebral organoids displayed abnormal electrophysiological activity like that seen in SMA SCOs. HD-MEA analysis (Fig. 4h, i) revealed that SMA cerebral organoids had a significantly lower basal spike frequency compared to CTRLs (2.206 ± 0.6826 Hz vs. 0.8497 ± 0.1629 Hz SMA, p < 0.0001; Fig. 4i). Consistent with the SCOs response observed after glutamatergic stimulation, SMA cerebral organoids increased their firing frequency by a significantly higher factor than CTRL organoids (115.3% ± 36.20 SMA vs. 39.11% ± 13.21 CTRL, p < 0.0001; Fig. 4i). Moreover, the presence of an active inhibitory neuronal circuit exploiting GABA-A (and likely glycine) receptors was detected (Supplementary Fig. 12d, e). Our results thus show pathological features in SMA cerebral organoids, demonstrating direct involvement of the disease in the CNS, beyond the spinal cord, and revealing the need to consider additional targets in the clinical therapeutic setting to evaluate the efficacy and the long-term response to treatments.

Optimized SMN-targeted approach effectively restores cellular and functional alterations
Among the approved therapies for SMA12, nusinersen is based on an antisense oligonucleotide (ASO) acting on SMN2 (ASO_10-27), increasing the overall levels of SMN protein. To determine if SMA SCOs are a suitable model for testing the effectiveness of potential therapies, and whether increasing SMN protein levels can correct specific molecular disease signatures, we tested a recently validated ASO for SMN2 with optimized morpholino chemistry (MO-10-3434,35) conjugated with an arginine-arginine-rich cell-penetrating peptide (r6-MO). We recently demonstrated that r6-MO can increase drug biodistribution and efficacy in animal models36.

First, we investigated whether unconjugated MO-10-34 and the r6-MO could increase SMN protein levels in SMA SCOs. SMA SCOs were treated at day 45 with either unconjugated (bare) MO or r6-MO (2.4 nmoles), or their respective custom-made scrambled sequences, for 48 h. This treatment was repeated three times with increasing doses (up to 4.8 nmoles) (Fig. 5a and Supplementary Fig. 13a). SMN reduction is a hallmark of SMA pathology (Supplementary Fig. 13b). We therefore first assessed whether our treatment could increase both SMN protein and mRNA levels. A significant increase in SMN protein expression was observed in lysates from r6-MO–treated organoids compared to untreated controls at later time points (Supplementary Fig. 13c). Biotin-tagged r6-MO was detected both at the periphery and within the central core of the organoids (Supplementary Fig. 13d), indicating effective and widespread tissue distribution. Consistent with this, treatment with either r6-MO or MO-10-34 (bare morpholino) led to a significant upregulation of SMN mRNA levels compared to their respective scramble controls across all treated lines (S1, S2, and S3) (Supplementary Fig. 13e, f). To specifically examine the expression of SMN1 and SMN2 isoforms, we performed restriction fragment length polymorphism (RFLP) analysis in CTRL, untreated SMA, scramble-treated SMA, and r6-MO–treated SMA lines. This analysis confirmed that r6-MO treatment significantly reduced the levels of the truncated SMN2Δ7 isoform in favor of the full-length transcript (Supplementary Fig. 13g).

Notably, the differentiation phenotype was completely restored by r6-MO treatment (percentage of cells positive for TUJ1: SMA 20.99% ± 8.044 vs. r6-MO 33.93% ± 7.648, p = 0.0003; Fig. 5b, c). The administration of r6-MO at day 45 was also able to significantly prevent cell death (percentage of apoptotic nuclei in each organoid at 2 months: untreated 18.16% ± 8.443 vs. r6-MO 10.58% ± 3.37, p = 0.005; Fig. 5d, e), supporting the idea that early intervention could halt the aberrant cellular processes, positively changing the course of the disease.

Strikingly, at the functional level, treatment with r6-MO resulted in enhanced basal activity compared to untreated SMA SCOs (r6-MO 1.843 ± 0.4396 Hz vs. untreated 1.067 ± 0.1623 Hz, p < 0.0001; Fig. 5f, g), which was paralleled by a reduced response to the glutamatergic stimulus (percentage increase in frequency r6-MO 44.76% ± 12.81% vs. untreated 98.31% ± 15.56%, p < 0.0001; Fig. 5f, g).

These findings reveal that fundamental cellular and functional phenotypes could be rescued by an early specific SMN-increasing therapy across multiple genetic backgrounds. Taken together, these data provide insights into the value of optimized ASOs and their impacts on the early treatment of SMA pathology.

SMA splicing defects are rescued by SMN-increasing therapy
Disease-modifying therapies are currently changing the natural history of SMA, but the specific molecular pathways affected by SMN-targeted approaches and what is their impact on the different cell types along the developmental trajectory are not fully elucidated. We leveraged our platform to dissect the molecular drivers responsible for the rescue in the cellular and functional phenotypes. The SMN protein has a well-recognized crucial role in the splicing machinery37. Thus, by RNA deep-sequencing, we investigated changes in splicing events in SMA SCOs, and assess the splicing modifications induced by the MO treatment. We analyzed CTRL (n=3 lines, 3 individual replicates), SMA (n=2 lines, 4 individual replicates), and SMA-treated SCOs (n=2 lines, 4 individual replicates) after a two-month differentiation period, following treatment with r6-MO and r6-scrambled-MO as negative control (Supplementary Fig. 14).

In SMA vs. CTRL SCOs, an overall transcriptional alteration was confirmed with 666 genes were upregulated and 463 were downregulated (Supplementary Fig. 14a and Supplementary Data 6). GO analyses revealed enrichment for progressive encephalopathy disorder, while showing a downregulation for synaptogenesis and glutamate receptor signaling (Supplementary Fig. 14b). These findings correlate with the alterations in functional activity displayed by SMA SCOs. Since bulk-tissue RNA-sequencing reveals averaged gene expression patterns across entire organoids, we employed our single-cell expression data to infer gene modules and understand co-expression in specific cell types. This analysis identified 5 distinct modules with genes enriched in specific cell types: cycling cells (M1: CDK1, CDKN3, NUSAP1, TOP2A, UBE2S), progenitors (M2: BTG1, EFNB1, HES2/4, TUBA1C), astroglia and progenitors (M3: GABBR2, NTRK2, PHGDH, PTPRZ1, SLC1A4), intermediate progenitors/early MNs (M4: FOXP4, RARA, SP1, TEAD2/3, TCF3), and mature MNs (M5: GRIA1/2/4, MAPT, NEFL, SCN2A) (Supplementary Fig. 14c and Supplementary Data 6). These data further suggest a pervasive phenotype, involving distinct developmental dynamics, where MNs, along with differentiating cells, are significantly affected by the lack of SMN1.

To uncover the molecular mechanisms downstream of r6-MO treatment and elucidate the pathways leading to functional rescue, we compared the transcriptional and splicing landscapes of r6-MO-treated SMA SCOs with those treated with r6-scramble-MO. The r6-scramble-MO treatment served as a negative control, providing a baseline for comparison with r6-MO treatment. Relatively few transcriptional changes were detected after r6-MO treatment compared to r6-scrambled-MO, suggesting that, at least within the considered time window, a global transcriptional change is not required for phenotypic rescue.

In contrast, analysis of pre-mRNA splicing patterns in SMA vs. CTRL, and r6-MO vs. r6-MO-scrambled treated SMA organoids showed substantial differences across conditions (Fig. 6a, b and Supplementary Fig. 14d). The loss of SMN1 in SMA patients SCOs (comparing CTRL with SMA organoids) and SMN rescue by r6-MO treatment (comparing r6-scramble-MO with r6-MO) led to multiple significant alternative splicing changes. The most altered events resulting were found as skipped exon (SE), mutually exclusive exons (MXE), followed by intron retention (IR), alternative 3′ (A3SS) and 5′ (A5SS) splice site events, detected with a lower frequency (Supplementary Data 7). To assess how r6-MO treatment affects the splicing landscape of SMA cells, we compared pre-mRNA splicing events in r6-MO-treated SCOs to those in r6-scramble-MO (baseline control) treated cells. A total of 2,614 splicing events resulted to be specifically modified by r6-MO (Fig. 6c and Supplementary Data 8). The most abundant ones were represented by SE, MXE, and IR in similar proportions to the ones observed for the comparison of CTRL vs. SMA. We could identify 1982 genes specifically associated with the 2614 rescued splicing events (Supplementary Data 8). Gene ontology enrichment analysis of these genes showed significant enrichment for regulation of chromatin organization, DNA metabolic process, and cell cycle, as well as axon guidance and neurodegeneration (Fig. 6c). Importantly, changes in splicing events could be aligned with specific gene modules from the single cell RNA-seq analysis (Fig. 6d). We leveraged all the detected rescued spliced genes to compute module score analysis, revealing unique combinations of spliced genes specifically associated with cycling cells (M1: BRIP1, CENPK, HMMR, TOP2A, UBE2C), progenitors (M2: ALG3, EIF5, LTN1, MRPL21, MRPL13), progenitors and neurons (M3: AHI1, CEP290, KIAA0586, VPS13C, XPO1) astroglia (M4: BMPR1B, DDX3X, PDGFRB, SIRT2, SLIT2) and MN cell types (M5: GRIA2, MAP2, SCN1A, SCN3B, STMN4) (Fig. 6d).

Our findings indicate lineage-specific splicing alterations downstream of r6-MO treatment (Supplementary Data 9). To assess whether these changes restore the splicing landscape of specific genes toward control levels, we compared splicing events related to the SMA phenotype vs. CTRL with those induced by r6-MO (carrying along the scramble-MO as specific control of the treatment). To do that, we intersected splicing events induced in SMA vs. CTRL and r6-MO vs. r6-scramble-MO conditions. This integrated analysis revealed a distinct set of splicing events rescued upon r6-MO treatment (Fig. 6e and Supplementary Data 9). Notably, genes outside this category (which did not fully recover their splicing pattern by the r6-MO treatment) were linked to broader pathophysiological processes, further suggesting a systemic involvement beyond spinal cord diseases (Supplementary Data 10). Early r6-MO treatment restored splicing isoforms in 98 genes (Supplementary Data 9), which were identified as top candidates in our bioinformatics analysis. These genes likely play a crucial role in alleviating the phenotypic defects of SMA, with several chromatin remodeling enzymes, ARIH2, ATG4A/B, CHD9, and HDAC6, emerging as key players. Over time, these targets may help re-establish the molecular profile of both progenitors and neurons, thus contributing to the observed cellular and functional rescue.

Among the rescued genes, we highlighted CHD9 (Chromodomain Helicase DNA-binding protein 9), a key regulator of the cell cycle in embryonic stem cells38, and HDAC6 (Histone Deacetylase 6), which is critical for motor neuron development and function through its roles in axonal transport, autophagy promotion, and neuroprotection39,40 (Fig. 6f, sashimi plots). These findings suggest that rescued splicing events impact diverse cellular and molecular processes crucial for progenitor and neuronal development, driving the observed functional recovery following r6-MO treatment.

Methods
The studies involving human samples were conducted in accordance with the ethical standards of the Declaration of Helsinki, national legislation, and institutional guidelines. Human fibroblast cell lines were obtained from Eurobiobank with informed consent, as approved by the ethical committee at Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico and University of Milan (0004520).

iPSC reprogramming and culture
Control iPSC lines C1 and C2 were derived from fibroblasts obtained from skin biopsies reprogrammed into iPSCs using the CytoTune iPS 2.0 Kit (CytoTune-iPS Sendai 2.0 Reprogramming Kit, Invitrogen). Control line C3 was obtained from Cedar Sinai CS5NXHiCTR. Control line C4 was obtained from WiCell iPS DF 19-9-11T.H and previously used in Corti et al.62,63. Control line C5 was previously generated from the Corti laboratory and published in Taiana et al.64. SMA line S1 was obtained from Cedars Sinai CS77iSMA. SMA line S2 was obtained from Cedars Sinai CS32iSMA. SMA line S3 was obtained from Cedars Sinai CS83iSMA. SMA line S4 was previously generated from the Corti laboratory65,66. SMA line S5 was previously generated from the Corti laboratory62. All iPSC lines were derived from male donors. IPSCs were maintained in E8 medium (Essential 8™ Medium, Gibco, A1517001) medium at 37°C in 5% CO2.

Embryoid bodies (EBs) formation
Embryoid bodies (EBs) were generated from iPSCs cultured in E8 medium (Essential 8™ Medium (Gibco, A1517001)) at 37°C in 5% CO2. At ~80–85% confluency, iPSC colonies were detached using Collagenase IV at 2 mg/mL (Gibco, 17104019), collected, and gently resuspended in complete EB medium (DMEM/F12 (Gibco, 11320033), KnockOut Serum Replacement (Gibco, 10828028), MEM-NEAA Solution (Gibco, 11140035), 2-mercaptoethanol [55 mM] (Gibco, 21985023), GlutaMAX (Life Technologies, 35050061)) supplemented with bFGF at 4 ng/mL (Peprotech, 100-18B). Cells were transferred to non-treated tissue culture Petri dishes to promote suspension growth. EBs were maintained in suspension for up to 7 days, with medium changes (EB medium without bFGF) every other day following manufacturer’s instructions (Pub. No. MAN0013784 Rev. 1.0).

Generation of cerebral organoids (CrOs)
Cerebral organoids were generated by adapting a previous protocol14. Selected iPSCs were harvested using Accutase (Life Technologies, A1110501) and approximately 9000 iPSCs were plated in each well of an ultra-low attachment 96-well plate (Corning, CLS7007) in pluripotent stem cell media containing low recombinant human FGF-basic (4 ng/ml, Peprotech, 100-18B) and Y-27632 (50 μM ROCK inhibitor Calbiochem, 13624S). On day 6, obtained embryoid bodies (EBs) were transferred to low adhesion 24-well plates (Corning, CLS3473) in neural induction media (NIM) containing DMEM/F12 (Gibco, 11320033), 1% N2 supplement (Gibco, 17502001), 1% GlutaMAX (Life Technologies, 35050061), 1% MEM-NEAA (Gibco, 11140035), and 1 μg/ml heparin (Sigma, H3149). On day 12, the EBs were transferred to droplets of Cultrex® Basement Membrane Matrix Type 2 (BME type 2, Trevigen, 3532-001-02) and grown in differentiation media containing a 1:1 mixture of DMEM/F12 and Neurobasal (Gibco, 21103049) with 0.5% N2 supplement, 1% B27 supplement without vitamin A (Gibco, 12587010), 2-mercaptoethanol, 0.025% insulin (Sigma, I9278), 1% GlutaMAX, and 0.5% MEM-NEAA. After 2 days, the droplets were transferred to a spinning bioreactor containing the same media except for B27 without vitamin A, which was replaced with B27 with vitamin A (Gibco, 17504044). The speed of the spinning bioreactor was gradually increased up to 45 rpm.

Generation of spinal cord organoids (SCOs)
To generate SCOs, the same procedure described for human cerebral organoids was applied from day 0 to day 2. On day 2, half of the medium was changed to fresh medium containing a final concentration of 4 ng/ml bFGF (Peprotech, 100-18B), 50 μM Y-27632 (ROCK inhibitor Calbiochem, 13624S), 3 μM CHIR 99021 (GSK3 inhibitor, Sigma-Aldrich, SML1046), 2 μM SB-431542 (Activin/BMP/TGF-β pathway inhibitor, Sigma-Aldrich, S4317), and 0.2 μM LDN-193189 (inhibitor of the bone morphogenetic protein [BMP] pathway, Stemcell Technologies, 72149). On day 4, the medium was replaced, adding the same small molecules (excluding bFGF and Y-27632). On day 6, the EBs were transferred into ultra-low attachment 24-well plates and cultured in NIM plus 100 nM retinoic acid (RA, Sigma-Aldrich, R3255). On day 12 EBs embedded in Cultrex and cultured with SCO differentiation media (SCODM, composed of 1:1 DMEM/F12 and Neurobasal with 0.5% N2 supplement, 1% B27, 2-mercaptoethanol, 0.025% insulin (Sigma, I9278), 1% GlutaMAX, and 0.5% MEM-NEAA). 1 μM Smoothened Agonist (SAG, Calbiochem, 364590-63-6) was added to the media. On day 14, SCOs were transferred to spinning bioreactors and cultured with SCODM supplemented with RA and SAG. On day 28, concentrations of RA and SAG were reduced to 50% and neurotrophic factors BDNF (Peprotech, 450-02), GDNF (Peprotech, 450-10), and IGF-1 (Sigma-Aldrich, 100-12) were added at 10 ng/ml each.

Immunohistochemistry analysis
Organoids were fixed with 4% paraformaldehyde (PFA) for 15 min at room temperature (RT) and subsequently placed in 30% sucrose/1× PBS overnight at 4°C. The next day, they were embedded in 10%–7.5% gelatin/sucrose, frozen in dry ice, placed at −80°C overnight, and cryosectioned into 20-μm sections using a cryostat. Organoid sections were permeabilized for 1 h in 1× PBS with 0.3% Triton-X (Sigma) and 10% donkey or goat serum (Jackson ImmunoResearch) at RT. 2D cultures were fixed with 4% PFA at RT for 5 min and permeabilized in 1× PBS with 0.2% Triton-X (Sigma) and 10% donkey or goat serum (Jackson ImmunoResearch) for 1 h at RT. Primary antibody diluted in fresh permeabilization solution was added overnight at 4°C (complete list summarized in Supplementary Data 2). The following day, secondary antibodies (Alexa Fluor 488, 555, 568, 594, and 647 conjugates, 1:1000; Invitrogen) were added with DAPI (1:500; Sigma-Aldrich, D9542) in 1× PBS for 90 min at RT. DAKO mounting media (Agilent, S3023) was used to mount the coverslips. TUNEL assay was performed using a DeadEnd™ Fluorometric TUNEL System (Promega, G3250). Confocal images were obtained using a Leica SP8 white laser inverted confocal microscope (Leica) and analyzed using Fiji image-processing software.

Image quantification
To evaluate the number of SOX2+ cells and the percentage of TUJ1-positive area, 10× mosaic images of whole SCOs sections were acquired using an Inverted Microscope DMi8 and the Leica LASX software platform. 20× mosaic images of whole brain organoid sections were imaged using Zeiss Axio Microscope and the Zen Light software platform. At least three different sections were acquired per organoid for at least 3 organoids/cell lines for n > 3 CTRL and SMA lines (Supplementary Data 1). In detail, to compute the number of SOX2+ cells, a custom-written Cell Profiler67 pipeline was designed, and a custom-designed ImageJ/Fiji68 software image macro was applied to evaluate the proportion of TUJ1+ area (as %). For TUNEL quantification, images were acquired by an inverted Nikon-Crest multimodal spinning-disk confocal microscope. Quantification of nuclei (DAPI) and TUNEL-positive cells was performed with an ad-hoc designed analysis pipeline for thresholding and segmentation using the GA2 module in Nis-Elements v5.21 software (Nikon-Lim Instruments). Regarding neurofilament tracing, images previously acquired with a Leica SP8 white laser inverted confocal microscope (Leica) were analyzed using Fiji image-processing software. The NeuronJ plug-in was used for the tracing and analysis of neurite length; only filaments that both originated and terminated within the field of view were traced. Filaments extending beyond the field of view or those for which the origin could not be determined were excluded from consideration.

Quantitative real-time PCR (qRT-PCR)
Total RNA was extracted using the ReliaPrep RNA Cell Miniprep System (Promega, Z6010). The RNA concentration was tested using Nanodrop (Thermo Fisher Scientific). Reverse transcription was performed using SuperScript IV VILO Master Mix (Thermo Fisher Scientific, 11756500). 150 ng of template total RNA were retrotranscribed. This protocol has been used to process bulk RNA samples. The expression level of candidate genes (Supplementary Data 3) was assessed by: (i) TaqMan quantitative analysis on the 7500 Real-Time PCR System and 7900HT Fast Real-Time PCR System (Applied Biosystem), GAPDH was used as a reference gene; (ii) Powertrack SYBR (Thermo Fisher Scientific) quantitative analysis on the ViiA7 Real-Time PCR System (Thermo Fisher Scientific), ACTB was used as a reference gene.

FL and Δ7 transcript analysis
Total RNA extracted from iPSC or SCOs were retrotranscribed using the SuperScript IV VILO Master Mix (Thermo Fisher Scientific). The relative expression levels of FL and Δ7 transcripts were assessed by RFLP analysis of RT-PCR products (primers available upon request). PCR products underwent o/n digestion at 37°C by DdeI endonuclease, followed by electrophoresis on a 2.5% agarose gel. DdeI recognizes and cuts molecules containing the SMN2 exon 8, allowing the discrimination of the genomic origin (SMN1 or SMN2) of FL and Δ7 isoforms.

Electrophysiology
HD-MEA was used to record the extracellular spontaneous activity of both SCOs and cerebral organoids. The device consists of 4096 planar electrodes measuring 21×21 μm, covering an area of 2.67×2.67 mm (Biocam X with Biochip Arena, 3Brain AG, Switzerland). Organoids were gently positioned on the chip and fixed with a platinum anchor and with a nylon mesh. Before and during recordings, organoids were submerged in Kreb’s solution with the following composition (mM): 120 NaCl, 2 KCl, 1.19 MgSO4, 26 NaHCO3, 1.18 KH2PO4, 11 glucose, 2 CaCl2, oxygenated with a mix of 95% O2/5% CO2 to obtain pH 7.4. Organoids were recorded under control conditions for 15 min, after addition of 100 μM of glutamate (L-glutamic acid, Sigma-Aldrich) for an additional 15 min, and after adding the combination of 100 μM glutamate, 10 μM gabazine (SR-95531, Abcam), and 1 μM strychnine hydrochloride (Sigma-Aldrich) for an additional 15 min. Brainwave X (3Brain AG) software was used to record the electrophysiological activity at the sampling frequency of 17 kHz. Brainwave 4 software (3Brain AG) was used to analyze the traces offline. The precise time spike detection algorithm was used to detect the spikes (parameters used: positive and negative peak, peak duration <1 ms, refractory period >1 ms, and detection threshold >7 times the standard deviation of the noise). Spike sorting was performed using a principal component analysis with K-means (silhouette method) considering a maximum three clusters per electrode and three spikes per cluster. The Pakhira-Bandyopadhyay-Maulik index was used for validating clustering results69. A final check out was performed by the researcher by re-analyzing the signals and removing any confounders. Only channels showing stable activity during the control period (without spike frequency fluctuations exceeding 20% spike frequency) were taken into consideration. The number of channels per organoid considered for the analysis was comparable between control (SCO 13.6 ± 0.6; cerebral 13.8 ± 2.1), SMA (SCO 15.2 ± 0.7; cerebral 15.4 ± 0.9), and treated SMA (SCO 12.8 ± 1.3) organoids. The percentage change in basal firing frequency was calculated by comparing the average firing frequency in the last 10 min before glutamate addition with the interval between 5 and 15 min after glutamate addition.

Morpholino treatment
We used a morpholino (MO-10–34) sequence targeting the SMN2 ISS-N1 region downstream of exon 7 that was previously described by our lab34. The specific MO sequence designed to target SMN2 pre-mRNA was GTAAGATTCACTTTCATAATGCTGG. The CPP used in the study is r6 (RRRRRR, D enantiomeric configuration), which was covalently conjugated to the 5′ end of the MO by our external collaborator, Dr. Hong Moulton. A scramble MO (scr, GTAACATTGACTTTGATATTCCTGG) was used as a control for antisense specificity. We used MO alone and conjugated with r6 peptide (r6-MO)36. As a negative control, we used a custom-made r6-MO containing the scrambled sequence. To assess biodistribution, we used a biotinylated r6-MO that was added to the culture medium as described below. All MO oligomers were synthesized by Gene Tools. We treated SMA SCOs with increasing concentrations of MO or r6-MO. The solution containing 2.4 nmol MO or r6-MO was added directly to each well and incubated for 48 h. Afterward, the culture medium was changed and fresh aliquots of 2.4 nmol MO or r6-MO were added for an additional 48 h. The medium was then replaced with fresh medium for 72 h. Lastly, the organoids were treated again with 4.8 nmol of MO or r6-MO for 48 h.

Western blot analysis
Western blot analysis was performed as described previously34. Briefly, samples were sonicated in lysis buffer supplemented with protease and phosphatase inhibitors (Pierce Rockford, IL, USA) on ice for 10 min. Approximately 25 μg of protein was separated by 12% SDS-PAGE and electrophoretically transferred to a nitrocellulose membrane. The membranes were incubated with anti-SMN (1:1000, BD) and anti-actin (1:1000, Sigma, St. Louis, MO, USA) antibodies. After labeling with peroxidase-conjugated secondary antibody (Life Technologies), the proteins were detected using a chemiluminescent substrate (Amersham, Pittsburgh, PA, USA). Signal was detected using LI-COR Odyssey 9120. Densitometry analysis was performed using Image Studio Lite (v5.2).

2D culture of cells from dissociated organoids
SCOs were centrifuged at 300×g for 5 min and trypsin-EDTA was added before transferring the organoids to a 37°C water bath for 5 min. The organoids were then mechanically dissociated, and a double volume of horse serum (Euroclone, ECS0091L) was used to inactivate the trypsin before centrifugation. The supernatant was discarded, and SCO differentiation medium was employed to resuspend the pellet. Approximately 50,000 cells were seeded in 24-well plates with coverslips previously coated with poly-L-ornithine (Sigma-Aldrich, P3655) and laminin (Sigma-Aldrich, L2020). SCODM supplemented with RA, SAG, and growth factors at the same concentration of differentiation day 28, was used for the maintenance of organoid-derived cells.

Whole mount staining
Organoids were washed in 1× PBS and then fixed in 4% PFA on a spinning wheel at 4°C overnight. The following day, five 1× PBS washes (10 min each) were performed on the spinning wheel at RT. Organoids were permeabilized with 0.25% Triton ×-100 in 1× PBS for 15 min. To prevent non-specific binding, organoids were incubated in 1× PBS with 10% FBS and 0.5% serum. Primary antibodies were replaced three times (after 2, 12, and 5 h) and were diluted in 1× PBS with 5% FBS, 0.5% serum of the same species as the primary antibodies, and 0.1% Triton ×-100. Secondary antibodies were diluted in the same solution used for primary antibodies and incubated overnight at RT on the spinning wheel. Images were acquired using a Nikon’s A1R MP+ multiphoton microscope by the Alembic Facility (IRCCS Ospedale San Raffaele).

SCO cell dissociation for single-cell RNA-sequencing analysis
Organoids were dissociated using an optimized protocol based on the Worthington Papain Dissociation System Kit (Worthington Biochemical). Single cell suspensions were collected on ice-cold 1× PBS (without calcium and magnesium) with 0.04% BSA (Sigma-Aldrich) at 1× 10^6/ml as counted by an automatic cell counter (Countess II, Thermo Fisher).

Single-cell RNA-sequencing and downstream analysis
Sample processing. Approximately 8000 cells from each sample, prepared as described above were loaded into one channel of the Single Cell Chip B using the Single Cell 3′ v3 reagent kit (10× Genomics) for gel bead emulsion generation in the Chromium system. Following capture and lysis, cDNA was synthesized and amplified for 14 cycles following the manufacturer’s protocol (10× Genomics). The amplified cDNA (50 ng) was then used for each sample to construct Illumina sequencing libraries. Sequencing was performed on the NextSeq 550 Illumina sequencing platform following the 10× Genomics instructions for read generation. A sequencing depth of ~45,000 reads/cell was obtained for each sample.

Data analysis. Raw sequencing data (bcl-files) were converted to fastq files using the Illumina bcl2fastq tool integrated into the CellRanger (10× Genomics) suite (v3.0.2). The CellRanger analysis pipeline was used to generate a digital gene expression matrix starting from raw data. Reads were aligned to the human GRCh38 reference genome, annotated, and counted with gene annotations from Ensembl version 93. The CellRanger count module was used to map reads with default settings, and the sequence length was set to r1-length=28 and r2-length=55. At least 90,000 reads per cell were produced. For further preprocessing and analysis, we used the Scanpy70 single-cell analysis toolkit (v=1.9.6, pandas v=2.2.0, and numpy v=1.26.3). Doublets were identified and removed with Scrublet71 (v=0.2.3). The threshold at doublet scores was automatically set by the Scrublet algorithm and was used individually for each sample. Quality control metrics, including mitochondrial and ribosomal content, were calculated using sc.pp.calculate_qc_metrics function, and then the object was filtered for the outliers cells containing less <10% mitochondrial and <5% ribosomal transcripts. Further, cells were filtered using a sample-specific median absolute deviation (MAD) threshold of “n_genes_by_counts”. After filtering, the data were concatenated using sc.AnnData.concatenate, annotated with sample names and genotypes. We used scVI-tools72 (v=1.1.0) to mitigate batch effects where each genotype and sample was treated as a batch. The AnnData object was first transformed by copying counts to data.layers[“counts”] and identifying 2000 highly variable genes using sc.pp.highly_variable_genes with the Seurat V3 flavor, considering the “Sample” batch key. The transformed data were prepared for scVI analysis by setting up AnnData with categorical covariates “Sample” and “genotype”, and continuous covariates “pct_counts_mt”, “pct_counts_ribo”, and “total_counts”. The scVI model was initialised with scvi.model.SCVI and trained using default parameters. The latent representation was extracted using model.get_latent_representation and stored in adata.obsm[“X_scvi”]. Dimensionality reduction was performed by constructing a KNN graph with sc.pp.neighbors using the latent representation (X_scvi) and running UMAP with min_dist=0.2. Clustering was executed at resolutions 0.25, 0.3, 0.5, 0.7, and 1.0 using the Leiden algorithm73 (sc.tl.leiden), and the results were visualized with UMAP plots (sc.pl.umap) displaying cluster assignments, sample information, and genotype. After integration, the data were reverted to all genes using adata.raw.to_adata(). Raw counts were normalized using sc.pp.normalize_total(adata, target_sum=None, inplace=False) and log-transformed with sc.pp.log1p as suggested in the recent sc-RNA-seq data transformation benchmark study74. The final processed data were saved for downstream analysis. Gene set scores were calculated using Scanpy’s sc.tl.score_genes function. To find DEG, we applied the Wilcoxon rank-sum test on the normalised counts between all found clusters (resolution=0.7), the top 50 genes sorted by the p-value and log-fold change were considered for the biological interpretation.

Differential abundance testing. We performed differential abundance testing using the Python version of Milo25 (v=0.1.1). Neighborhoods were constructed with a proportion of 0.1 using milo.make_nhoods. Neighborhood counts were computed with milo.count_nhoods using the sample column “Sample”. Both genotypes (CTRL and SMA) were encoded as continuous and categorical variables. Differential abundance testing was conducted using milo.DA_nhoods with a design formula based on genotype. A neighborhood graph was built using milopy.utils.build_nhood_graph and visualized with milopl.plot_nhood_graph, highlighting the spatial false discovery rate (FDR) at 1%. Neighborhoods were annotated with cell types using milopy.utils.annotate_nhoods and the color palette from Leiden clustering was mapped to the neighborhoods.

Pseudo-bulk differential gene expression analysis between SCOs genotypes. To test for DEG between genotypes for a particular cell identity (at least 25% of cell proportion for each genotype), we leveraged the PyDESeq275 (v=0.4.4) and the Python implementation of decoupleR76 packages (v=1.6.0). Pseudo-bulk profiles were generated using the decoupler.get_pseudobulk function with a minimum of 10 cells and 1000 counts per group. The pseudo-bulk data were normalized, log-transformed, scaled, and principal component analysis (PCA) was performed. Associations between metadata and principal components were computed using decoupler.get_metadata_associations, and the results were visualized with decoupler.plot_associations. Data were subset by cell type, and differential expression analysis was performed on selected cell types. Genes were filtered by expression with a minimum count of 10 and a minimum total count of 15. The DESeq2 object was constructed with the design factor “genotype” and reference level “CTRL”. Log fold changes (LFCs) were computed, and contrasts between SMA and CTRL genotypes were extracted. DEG were filtered by adjusted p-value (<0.05) and absolute LFC (>0.5). The results then were visualised in the form of complex heatmaps and volcanoplots with custom R scripts. The lists of DEG of selected cell types was analyzed using the Ingenuity Pathway Analysis (IPA—Qiagen Ingenuity Systems) to compute canonical pathways and diseases and functions enrichment.

Prediction of SCOs differentiation states and pseudotime analysis. We performed the prediction of developmental potential on the integrated data using CellRank’s77 (v=2.0.0) implementation of CytoTRACE78. The data were preprocessed by ensuring availability of spliced and unspliced RNA counts, with these layers established from the primary expression matrix when missing. Moments of spliced and unspliced abundances were computed using scvelo.pp.moments. Lately, the CytoTRACE kernel from CellRank was applied to calculate the CytoTRACE score and construct a corresponding transition matrix. CytoTRACE pseudotime was derived from the computed scores to quantitatively represent cellular differentiation status. Pseudotime scores were visualized as density plots comparing different genotypes. Density distributions were estimated using Gaussian kernel density estimation (scipy.stats.gaussian_kde) and plotted as ridgeline plots, color-coded by genotype and cell type. Visualization and figure customization were performed using the Matplotlib library in Python. To statistically compare pseudotime distributions between genotypes (CTRL vs. SMA), a two-sided KS test was performed using SciPy’s ks_2samp function. Pseudotime values for each genotype were extracted, and the KS statistic and associated p-value were computed to assess differences in distribution between groups.

Regulons inference and trajectory analysis of motoneurons lineage. To infer the co-expression modules between transcriptional factors and putative target genes in MN cell lineage, we used the pySCENIC (v=0.12.1) pipeline28. We performed independent pipeline runs for each SCOs genotype data object with the Sungularity container. At the first step of regulons inference, the pipeline relies on a machine learning model that fits the expression patterns of genes to expression patterns of TFs by the GRNBoost279 algorithm. For this part, we used the command line tool (pyscenic:0.12.1 arboreto_with_multiprocessing.py) with the default parameters. Next, the output fits were used to predict the potential regulons and direct-binding targets that are then refined through TFs motif enrichment analysis with (pyscenic:0.12.1 pyscenic ctx) with “–maks dropout” parameter. Lastly, cells were scored for each regulon activity in SMA and CTRL objects, respectively via the AUCell method80 with (pyscenic:0.12.1 pyscenic aucell). Obtained regulons activities scores and metadata were used in the secondary analysis for visualisation and trajectory analysis. We performed trajectory analysis on SCOs focusing on motoneuron linage using Palantir81 (v=1.3.3) and scFates82 (v=1.1.1). The SCOs data object was loaded, and specific clusters unrelated to the MN lineage were removed. Diffusion maps were computed to generate a multiscale diffusion space with four eigenvectors. The principal tree was constructed using scf.tl.tree with parameters method=“ppt”, Nodes=200, ppt_lambda=100, ppt_sigma=sig, ppt_nsteps=200. The root was set to the SOX2 expression using scf.tl.root and corresponded to the pMN. Later, the pseudotime was computed using scf.tl.pseudotime with n_jobs=20, n_map=50, seed=42. To explore the differences in motoneuron differentiation along a shared trajectory, the data were stratified by genotype into CTRL and SMA MN groups. For each group, dendrograms were generated using scf.pl.dendrogram and KDE plots (seaborn.kdeplot). Next, the pySCENIC AUCell output was processed to align with the motoneuron trajectory data generated before and this approach was adopted from the study of Petitpré C et al.83. pySCENIC AUCell output for control and SMA samples was loaded from .loom files. The AUCell matrices were aligned with the trajectory data by matching cell IDs. Regulons active in fewer than six cells were filtered out. Regulon activity was tested for association with trajectory data using scFates and GAM (scf.tl.test_association with n_jobs=20 and A_cut=0.025). Pseudotime for cells was updated based on their order in trajectory analysis (adata.uns[“pseudotime_list”]), and the trajectory model was fitted (scf.tl.fit with n_jobs=20). The trend of specific regulons activity along the trajectory segments was visualised using scf.pl.single_trend and scf.pl.trends. For control samples, a set of significant regulons was highlighted, and the trends of these regulons were plotted. Similar analyses were performed for SMA samples.

Identification of informative splicing gene modules. To associates splicing gene modules to clusters in the SCOs object (CTRL or SMA), we subsetted the object to contain only the selected pools of splicing genes, but all cells. Using scVI dimensionality reduction, Hotspot84 (v=1.1.1) analysis was conducted to identify gene modules by calculating autocorrelations for the genes and identifying significant genes with an FDR < 0.05. Local correlations were computed for the significant genes using 15 parallel jobs, and gene modules were created with a minimum gene threshold of 50, core-only mode, and an FDR threshold of 0.05. Module scores were calculated and assigned to the dataset. Finally, module scores of splicing genes were visualised using scatter plots on UMAP coordinates with custom color palettes.

Stage mapping to reference fetal spinal cord data. To map our control organoid dataset onto a temporal developmental reference, we first processed a fetal spinal cord single-cell atlas23 using Scanpy. After standard cell and gene filtering, the data was normalized, and 2000 highly variable genes (HVGs) were identified using a batch-aware “seurat_v3” approach. We then trained a four-layer scVI model on these HVGs to create a latent reference space. Our control organoid query data was subsequently aligned to this 2000-geneset by adding missing genes with zero expression. Using the scArches (v=0.6.1) package projected the query data onto the reference atlas via partial model retraining. The developmental stage of CTRL organoid cells was then inferred by transferring “Gestational Week” and “Trimester” labels from the atlas using a weighted-kNN classifier (k=100), and we quantified the similarity between query and reference states by calculating cell presence scores.

Cell types mapping to reference fetal spinal cord data. First, the raw count matrix of fetal spinal cord atlas24 was loaded from a .h5ad file into an AnnData object. The raw counts were preserved in a separate layer, after which the data was normalized to a total of 10,000 counts per cell (normalize_total) and subsequently log-transformed (log1p). The top 2000 highly variable genes were identified across batches using the seurat_v3 method on the raw count layer, with the dataset being subset to these genes. To correct for batch effects associated with developmental stages, we employed the scVI-tools package (v=1.3.0). The model was set up using the raw counts and the stage annotation as the batch key. The scVI model was initialized with two hidden layers and trained to learn a latent representation of the cells. This latent space embedding was then used as the basis for semi-automatic annotation. To assign broad cell identities, gene module scores were calculated based on marker genes adapted from ref. 24. These scores enabled the classification of major tissue types, including spinal cord progenitor, spinal cord neuron, sensory neuron/progenitor, mesoderm, hematopoietic, and erythrocytes, as visualized in ref. 24.

Reference model pre-training. A pre-processed and annotated fetal spinal cord dataset was used for scPoli85 model training, a deep generative model designed to integrate datasets from different conditions while preserving cell type identity. We first identified the top 2000 highly variable genes (HVGs) across experimental batches (stage key) using the seurat_v3 method. The scPoli model was initialized on the data subset containing only the HVGs. The model was configured using stage as the condition key and manually assigned cell types as the cell type key. The architecture was defined with a 32-dimensional latent space and a single hidden layer of 1024 nodes. A Zero-Inflated Negative Binomial loss function was used for data reconstruction. The model was trained for a maximum of 50 epochs, with 40 pre-training epochs and an early stopping mechanism which monitored the validation prototype loss with a patience of 10 epochs. Upon completion of training, the learned latent representation was extracted and stored in the original AnnData object. This integrated “X_scpoli” embedding was then used for transfer learning and downstream analysis, including the computation of a k-nearest neighbor graph and visualization with UMAP. To compare our spinal cord organoid dataset with an in vivo reference, we performed computational mapping using a pre-trained scPoli model built on fetal spinal cord atlas. First, the query organoid dataset was loaded, and cells from control (CTRL) samples were isolated for the analysis. To ensure compatibility with the reference model, the query data’s gene set was aligned to the 2000 highly variable genes used to train the reference. Genes present in the reference but absent from the query were added with zero-count expression values. We utilized an scArches86 (v=0.6.1) workflow to project the query data into the reference latent space. The mapping was performed using a partial retraining strategy (retrain=“partial”) for 30 epochs, which fine-tunes the model on the query data while keeping the reference cell embeddings fixed. Following the mapping, we constructed a weighted k-nearest neighbor (wknn) graph in the shared latent space, connecting each query cell to its k=100 nearest neighbors in the reference atlas with HNOCA-tools package87 (v=0.1.1). This graph formed the basis for label transfer, where cell type annotations from the reference were assigned to each organoid cell, yielding the final labels and a quantitative confidence score. The resulting latent space coordinates and transferred annotations were then added back to the original query object for further analysis. Secondly, we calculated cell presence scores for primary tissue to quantify the compositional similarity between the organoid sample and the fetal reference atlas. The resulting latent embedding and annotations were added to the query object for downstream visualization and analysis.

Bulk RNA-seq analyses
Library construction. Libraries were prepared starting from 50 ng of total RNA derived from 9 control, 6 SMA, and 16 morpholino-treated SMA (8 r6-MO, 8 r6-MO-scrambled) organoids, using the SMARTer Stranded RNA-Seq Kit, TaKaRa) and following the manufacturer’s instructions. Samples underwent a high coverage paired-end 150-bp strand-specific sequencing using a NextSeq 2000 platform (Illumina).

Alignment. For each sample (9× control organoids, 6× SMA organoids, 8× SMA organoids treated with r6-MO, and 8× SMA organoids treated with r6-MO-Scramble), raw reads were mapped to the human hg38 genome and counted using STAR v2.7.10a88.

SMN1 and SMN2 counts. SMN1 (SMN-C) and SMN2 (SMN-T) genes are characterized by a very high degree of homology (more than 99%). Thus, to distinguish the reads derived from the two genes, we counted the number of reads mapping to exon 7, where the presence of a nucleotide difference between the two genes enables their unambiguous identification. We performed this check using the IGV tool. Counts mapping elsewhere on the SMN genes were not considered for this analysis.

Differential gene expression analysis. Differential gene expression analysis was carried out using R and the edgeR package v4.0.2 with the functions estimateDisp, glmFit, and glmLRT. Genes having cpm < 0.05 in at least 6 samples were filtered out.

Gene ontology differential expressed genes. Gene ontology of differential expressed genes between CTRL and SMA organoids was done using Ingenuity Pathway Analysis. Terms from “Canonical pathways” and “diseases and functions” were filtered.

Differential splicing analysis. Differential splicing analysis between samples was determined using rMATS turbo v4.3.089, a tool that identifies 5 basic types of AS patterns: SE, MXE, alternative 5′ (A5SS) and 3′ splice sites (A3SS), and retained introns (RI). Significant events were characterized by a FDR < 0.05 and an absolute Inclusion Level Difference > 0.05. Only the reads mapping to exon-exon junctions were used for the analyses. Sashimi plot of events of interest were created using rmats2sashimiplot v2.0.4 (https://github.com/Xinglab/rmats2sashimiplot).

Gene ontology of splicing events. Differential splicing events from SMA vs. CTRL and SMA r6-MO vs. SMA r6-MO-Scrambled were compared. Events sharing (i) the same starting position and the same ending position and (ii) with opposite Inclusion Level Difference were called as “common events”. To annotate gene function, gene ontology was performed using Metascape90. Downstream processing of splicing and gene expression data were done in R v4.1.0 using custom scripts.

Statistical analysis
Continuous variables were reported as mean ± SD. Comparisons between groups and between baseline and post-intervention or interval within a group were assessed using unpaired and paired two-sided Student’s t-test, respectively. One-way or two-way ANOVA was used for comparisons among groups, with Tukey HSD or Sidak’s post-hoc tests applied when the null hypothesis for the omnibus analysis was rejected. GraphPad Prism v.8 was used for all analyses with a significance level of 0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Remarks to the authors.\nThe study from Faravelli and Rinchetti et al. uses SMA patient-derived iPSCs to generate spinal cord and cerebral organoids to study disease phenotype at early stage of neuronal maturation. Moreover, the authors test the possibility of an antisense oligonucleotide therapeutic strategy to rescue the early disease phenotype in patient-derived spinal cord organoids (SCOs).\nThis study is not the first to generated SCOs to study SMA [PMID: 30368521], but presents a number of novelties: 1) the single cell RNA sequencing analysis, which provides an in depth characterization of control and SMA derived SCOs at early developmental stages, indicating a possible developmental phenotype; 2) the generation of SMA cerebral organoids and the study of cortical-related dysfunction; 3) the electrophysiological analysis is the first of its kind in SMA-derived SCOs and cerebral organoids; 3) the evaluation of antisense oligonucleotides efficiency in patient-derived SCOs. Thus, this study sounds interesting and would greatly improve the field. However, some major points in the study design, quality of data collected and depth of the analysis, significantly hamper the overall value and would merit further attention.\nComment 1\nThe authors do not justify and comment the donors cohort from which iPSC have been generated.\n• SMA patient iPSC lines have been generated from paediatric or foetal samples, while healthy control lines have been derived from donors at different ages spamming from newborn to 34 years old (SUPPLEMENTARY TABLE 1 - Characteristics of the human fibroblast-derived iPSC lines). What is the rational for this choice?\n• When choosing single donors for downstream analysis, SCOs derived from adult donor iPSCs are compared to organoids derived from paediatric/foetal SMA donors (Extended data figure 4 -figure legend). Age-matching of control and patient samples is the best practice modelling diseases.\n• No explanation is given for the choice of a specific donor-derived iPSC line compared to another in different analysis.\n• No information is given about the severity of the donor phenotype. This is essential for a disease like SMA where the phenotypic spectrum is broad.\n• No proper characterization of the generated iPSC lines is provided. Analysis of Genome integrity, maintenance of the mutation in SMN1, pluripotency evaluation, and transgene elimination should be provided for each line used in the study.\nComment 2\nThe authors have generated iPSC lines from 7 healthy controls and 7 SMA type I patients. This number of controls and patients is sufficient for the study of genetic disorders in the absence of patient isogenic lines, which are impossible/difficult to generate for SMA type I. Despite this, the authors used only iPSC lines from few donors (e.i. 2 control and 2 SMA donor sc-RNA seq; 2 control and 3 SMA donors for FIG 1d). This number is not sufficient to drove any conclusion and to achieve a proper statistical power. Indeed, differences observed might simply related to the donor genetic background. Moreover, the authors stated that they use n ≥10 organoids derived from control or SMA iPSC lines, but no indication is given on the exact number of organoids from each donor-iPSC line and if they were derived from independent differentiation. A good practice for the use of iPSCs in research and in particular for modelling genetic disorders, has been recently published by the ISSCR [https://www.isscr.org/standards-document].\nComment 3\nIt is not clear what is the rational for the scRNA-seq annotation. SUPPLEMENTARY TABLE 4 - List of the top 50 genes… reports main genes in each cluster, but it is no provided a clear indication of which genes have been considered specific to each cellular subtype and used for the annotation. Despite this, the authors identify, for instance, four different groups of motor neurons, but is not clear what drove this specification. More detailed should be provided. Moreover, how the author scRNA-seq data set aligns to other similar analysis in SCOs [PMID: 35347276]?\nComment 4\nMost of the immunofluorescence images are below an acceptable quality and fail to properly represent quantitative data or prove co-localization of protein expression, thus hampering proper characterization. For instance: 1) SOX2 staining in Figure 2a SMA panel seems to show increased positive cells in the patients compared to control, or unspecific staining; 2) Figure 2c does not show an increased TUJ1 positivity in the SMA patient SCOs; 3) in Figure 3d is not clear what has been measured to quantify the neurites length, neither proper information is given in the figure legends; 4) Extended Figure 5a is not proving any positivity for TUNEL analysis, while it mostly shows unspecific and spatial restricted signal, which might lead to bias in the interpretation (how quantification has been taken?); Extended Figure 9c immunofluorescence for GAD65 is not really showing believable neurons, and control and SMA images seems at different magnification.\nComment 5\nSome of the conclusion in the results section do not appear properly justified by the data provided. For instance, page 7 lines 208-217, the solely use of the TUNEL technique does not allow conclusion of cellular death process. Beside the not convincing representative immunofluorescence analysis (see Comment 4), other analysis should have been provided to identify the population undergoing cell death (motor neurons, progenitors, other neurons?). Moreover, Page 9 lines 283-285, the authors stated that transcriptional alteration in SMA does not relate to cell death. It is not clear if neurodegeneration occurs and, if so, at what stage of SCOs maturation.\nHow the increased annotated groups ‘progenitors and neurons’ observed in scRNA-seq analysis (Figure 1e and h, Figure 2j and Extended Figure 5e-f) fit with the data reporting no difference in the SOX2 positive cells and decreased TUJ1 positivity in 2 months old SMA SCOs?\nPage 13 line 398 the conclusion that unconjugated MO does not rescue phenotype due to increased organoids size is not demonstrated. Testing in smaller organoids should be provided. Moreover, this set of experiments should have included a scrambled oligonucleotide to side to untreated.", "reviewer_2": "In this study, Faravellii et al compared human SMA type 1 “spinal cord organoids” and “cerebral organoids” from multiple donors. Single-cell RNA-seq revealed changes in the gene expression in various cell types, not only in motor neurons. These included neural progenitors and showed pathological changes in neuronal differentiation. Multi-electrode array analysis showed abnormalities in the activity of both SMA spinal and cerebral organoids. This suggests that SMA pathogenesis could be widespread throughout the central nervous system. The use of antisense oligonucleotides targeting SMN expression in the early stages of organoid development showed a rescue of both morphological and functional deficits in SMA spinal cord organoids. Erroneous splicing in SMN1-target mRNAs was corrected by early treatment.\nOverall the manuscript provides important insights into early pathogenesis mechanisms of SMA, which could not be easily assessed without using human iPS cell-derived neural organoids. The comparison between “spinal cord” and “cerebral” organoids could highlight similarity and difference of the disease-induced alterations. Antisense oligonucleotide treatment of organoids demonstrated treatment mechanisms at cellular and molecular levels. The manuscript gives important insights into SMA pathophysiology and disease modeling methodology. However, I have concerns and comments on the manuscript as below.\nComments:\nThe “spinal cord organoids” are characterized by immnunostaining and scRNAseq. Please compare percent of key cell types (motorneurons, inhibitory neurons, excitatory neurons, progenitors, glia etc) observed by immnunostaning and scRNAseq so that the cellular composition of the organoids.\nComparison of the cellular composition between spinal cord organoids and cerebral organoids is critical as basis for the overall study.\nPlease include HB9 for identification of motor neurons.\nRetinoic acid-induced ventralization could reduce other types of cells in the organoids. Please discuss how retinoid acid-mediated differentiation was controlled in the spinal organoids. Figure 4a indicates SAG and RA are always added to the culture media. Was this intended to maximize motor neuron differentiation and maintenance?\nFigure 3d indicated reduction of axon length in SMA neurons from spinal cord organoids. Was this similar in neurons from cerebral organoids?\nPlease explain the y-axis of Figure 4d in more detail. Was it “increased” percentage from the base line? Was it measured right after adding glutamate directly onto the organoids? Please also provide representative traces and raster plots.\nAlso was Extended Figure 10i analyzed at similar time point with Figure 4d? Representative traces should be presented to know temporal changes.\nWas active number of electrodes different between SMA, control organoids, and MO-treated SMA organoids?", "reviewer_3": "In this study, the authors use control and spinal muscular atrophy (SMA) spinal cord and cerebral organoids to examine the evolution of gene expression abnormalities over developmental time using single cell sequencing and to assess changes in pre-mRNA splicing patterns after treated with a splice switching antisense oligonucleotide. Emerging data indicate that SMA may start in utero with developmental impairments that are associated with vulnerability to rapid degeneration. Understanding the molecular events underlying these developmental and degenerative events are key for understanding how to optimize therapy.\nThe major findings of this study are 1) SMA spinal cord organoids may be a useful model of human SMA in vitro 2) there are histological and gene expression signatures suggesting very early developmental slowing involving neuronal and nonneuronal cells and 3) a small proportion of abnormal splicing events improve with ASO treatment. Important strengths of the paper are the effort to develop a human SMA spinal organoid model as prior attempts to look at 2D iPSC derived MNs have not clearly revealed disease relevant pathologies. Further the detailed sequencing of human cells has the potential to reveal new insights into disease pathogenesis. Unfortunately, in its current form, there are several experimental limitations (see below. Although the observation of developmental slowing is interesting, it is not clear whether these findings are relevant for the human disease and there is no mechanistic investigation of the identified gene expression changes to validate their importance.\nSpecific comments:\n1) Several aspects of the initial CNTL and SMA organoid characterizations need to be strengthed. In an extended table, 6 CNTL and 7 SMA lines are listed ranging in age from newborn to 34 years for CNTL and fetal to 2 years for SMA. Only some of these lines are actually used in the paper and for most experiments only 1 to 2 lines each are used. It must be clarified which lines are used for which experiments and whether the differences in subject age play an important role in the behavior of the iPSCs. Further, a minium of 3 lines each (with duplicate derivations) should be used for most experiments. No information regarding SMN1/SMN2 copy number or SMA type is provided. There is no baseline assessment of SMN mRNA or protein levels amongst the different lines over differentiation time (the authors make the point in the intro that SMN may decrease in whole human tissues over developmental time). Although key markers of iPSC differentiation are quantified over differentiation time in several CNTL lines, this quantitative data are not provided for the SMA lines (Ext data figure 1).\n2) During the initial characterization of the SMA organoids, the authors note a transcription profile that correlates with Carnegie Stage CS17. They refer to this as a human fetal stage, but in fact this classifcation refers to human embryo staging (first 10 weeks of gestation are embyro and 10-40 weeks are fetus). This is an important distinction as this implies that the organoids are VERY IMMATURE. Although studies of human type I SMA tissues indicate MN developmental abnormalities at mid-late fetal stages, I am not aware of abnormalities dating to the embyro. How do the authors conceptualize the developmental processes in these organoids relative to human SMA development.\n3) There seems to be a disconnect between the number of SOX2+ neural progenitors at 1 month and 2 month (CNTL and SMA are equivalent at 2 months) compared to the number of apoptotic cells at these same time points. The authors need to provide the statistical comparison between CNTL and SMA in number of apoptotic cells at 1 mo and 2 months. What cell types are actually dying at 2 months? Do MNs actually undergo apoptotic cells death or is this another cell type?\n4) The authors note that there are many gene expression abnormalities between SMA and CNTL, but identify only one gene (NRXN2) that has been previously reported to be altered in SMA tissue. A formal comparison to other data sets might help strengthen the relevance of these gene expression changes.\n5) SMI32 is used to define axon length. More thorough analysis to prove these are MNs with MN specific markers and stains that would specifically identify axons vs. dendrites. As is perhaps more accurate to call neurites?\n6) Extended data 9 includes only 1 CNTL line (see comment about n above).\n7) There is inadequate characterization of the cerebral oraganoids with no quantification of the basic markers over differentation time. Very similar histological and electrophysiological phentoypes are reported in these organoids as reported in the SC organoids. Although the brain may be affected in some very severely affected SMA patients, it is much less severely affected than the spinal cord. It would have been much more impactful to see divergent phenotypes between these two organoid systems-why is this not seen?\n9) The treatment experiments as currently presented are not sufficiently rigorous. CNTL lines need to be run in parallel to assess nonspecific changes arising from treatment and as a reference point for SMN expression. A scrambled ASO is also needed. The targeting of the ASO to cells in these organoids need to directly assessed. How many cells and what cell types actually took up the ASO? Were the modest improvements in splicing patterns due to inadequate drug distribution or splicing changes no longer reversible by SMN induction? Those genes that can not reverse their splicing pattern even after full SMN repletion would be very interesting for the SMA field to identify particularly if they are relevant to disease manifestations in patients."}, "author_response": "We thank the Reviewers for their enthusiasm for our work and for their thoughtful comments and constructive feedback for the revisions. We have now addressed all the comments through extensive new data and analyses, as well as text revisions. Here, we first highlight the major changes to the revised manuscript and then address each of the Reviewers' comments point-by-point.\n• We have profiled the iPSC lines generated de novo for this publication and specified the features of the commercial and previously published lines. This sums up to 5 iPSC patient and control lines on which most of our analyses have been conducted. This resulted in a new supplementary figure and a new supplementary table (Extended (Ext.) Data Fig. 1 and Supplementary Table 1).\n• We have generated extensive new data on brain organoids showing a robust functional phenotype, but not a difference in the general amount of SOX2 positive progenitors at the analyzed time point (Fig. 4 and Ext. Data Fig. 9). Our study therefore proposes, for the first time, a direct investigation of brain SMA phenotype and highlights an important difference between brain and spinal cord organoids in the context of SMA pathogenesis.\n• As requested, we have increased the number of biological replicates and revised all the immunostaining panels and relative quantification for clarity, adding a color code system to specify which lines were used for each analysis.\n• We have also extended our results with numerous new analyses to further support our findings: we added new electrophysiological replicates to all the functional analyses, we updated our single cell RNA sequencing (scRNA-seq) datasets with novel computational tools, and we added novel computational analyses for the developmental trajectory and splicing activity.\n• We also included novel experiments demonstrating the splicing alternative patterns induced by SMA alterations and rescued by the antisense oligonucleotide treatment. We leveraged a customized scramble sequence to further validate our results, and we repeated all the experiments. In addition, to address the biodistribution pattern of the ASO treatment, we employed a customized biotinylated ASO sequence and verified the extent of the treatment distribution in spinal cord organoids.\nOverall, with the addition of these data and analyses, we are confident that we have addressed all the points raised by the Reviewers. These data further reinforce our conclusions on progenitor involvement in the pathology and the pervasive cellular response to a clinically meaningful molecular treatment.\nWe address the Reviewers' specific comments below.\nReviewer #1 (Remarks to the Author):\nRemarks to the authors.\nThe study from Faravelli and Rinchetti et al. uses SMA patient-derived iPSCs to generate spinal cord and cerebral organoids to study disease phenotype at early stage of neuronal maturation. Moreover, the authors test the possibility of an antisense oligonucleotide therapeutic strategy to rescue the early disease phenotype in patient-derived spinal cord organoids (SCOs).\nThis study is not the first to generated SCOs to study SMA [PMID: 30368521], but presents a number of novelties: 1) the single cell RNA sequencing analysis, which provides an in depth characterization of control and SMA derived SCOs at early developmental stages, indicating a possible developmental phenotype; 2) the generation of SMA cerebral organoids and the study of cortical-related dysfunction; 3) the electrophysiological analysis is the first of its kind in SMA-derived SCOs and cerebral organoids; 3) the evaluation of antisense oligonucleotides efficiency in patient-derived SCOs. Thus, this study sounds interesting and would greatly improve the field. However, some major points in the study design, quality of data collected and depth of the analysis, significantly hamper the overall value and would merit further attention.\nWe thank Reviewer #1 for their insightful comments and constructive suggestions on our study. We are pleased that Reviewer #1 finds our work to be both interesting and novel, and recognizes its potential to significantly advance the field. The suggestions to enhance the study design and deepen the analysis was highly valuable and have contributed to improving the quality and rigor of our manuscript.\nBelow, we provide a detailed point-by-point response to each of the Reviewer’s comments. We believe that these revisions comprehensively address the Reviewer’s concerns, further strengthen our findings, and highlight the overall significance of the study.\nComment 1\nThe authors do not justify and comment the donors cohort from which iPSC have been generated.\n• SMA patient iPSC lines have been generated from paediatric or foetal samples, while healthy control lines have been derived from donors at different ages spamming from newborn to 34 years old (SUPPLEMENTARY TABLE 1 - Characteristics of the human fibroblast-derived iPSC lines). What is the rational for this choice?\n• When choosing single donors for downstream analysis, SCOs derived from adult donor iPSCs are compared to organoids derived from paediatric/foetal SMA donors (Extended data figure 4 - figure legend). Age-matching of control and patient samples is the best practice modelling diseases.\n• No explanation is given for the choice of a specific donor-derived iPSC line compared to another in different analysis.\n• No information is given about the severity of the donor phenotype. This is essential for a disease like SMA where the phenotypic spectrum is broad.\n• No proper characterization of the generated iPSC lines is provided. Analysis of Genome integrity, maintenance of the mutation in SMN1, pluripotency evaluation, and transgene elimination should be provided for each line used in the study.\nWe have carefully considered the Reviewer’s suggestions regarding human iPSC characterization and have made substantial changes to the data visualization and analysis. These valuable suggestions from the Reviewer have significantly strengthened the quality of our work.\nTo address these points, we have now provided an extensive characterization of all the iPSC lines generated de novo for this study. This includes assessments of 1) morphology, 2) genome integrity, and 3) self-renewal markers, as shown in the newly generated Ext. Data Fig. 1. The same parameters were applied to representative commercial lines used as internal controls. For the commercial and previously published lines, we have included more detailed information about their sources and features.\nAdditionally, we extensively characterized the SMN molecular phenotype of the iPSC lines. This includes qPCR analyses to assess SMN copy number, evaluations of the ratio of FL/D7 SMN isoforms, and SMN expression levels over time in culture. We also provided more detailed clinical information about the phenotypes of each donor/patient.\nWhile there is currently no evidence that iPSC-derived cultures from age-matched donors confer a specific advantage for induced differentiation, previous studies have shown that age-related “memory” in the form of epigenetic signatures can persist in iPSCs derived from individuals of different ages (PMID: 27941802). However, these differences are largely negligible after extensive passages during reprogramming (>8 passages), which is consistent with the passage numbers for all the lines used in this study. Nonetheless, we included pediatric lines in our control cohort to ensure a degree of age-matching and further robustness in our analyses.\nWe also increased the number of lines used in the experiments and clarified their allocation in the various analyses, as highlighted in the newly generated Supplementary Table 1 and throughout the manuscript. This required the generation of new batches of spinal cord and brain organoids derived from both CTRL and SMA lines, ensuring consistency and reproducibility across all experiments.\nComment 2\nThe authors have generated iPSC lines from 7 healthy controls and 7 SMA type I patients. This number of controls and patients is sufficient for the study of genetic disorders in the absence of patient isogenic lines, which are impossible/difficult to generate for SMA type I. Despite this, the authors used only iPSC lines from few donors (e.i. 2 control and 2 SMA donor sc-RNA seq; 2 control and 3 SMA donors for FIG 1d). This number is not sufficient to drove any conclusion and to achieve a proper statistical power. Indeed, differences observed might simply related to the donor genetic background. Moreover, the authors stated that they use n ≥10 organoids derived from control or SMA iPSC lines, but no indication is given on the exact number of organoids from each donor-iPSC line and if they were derived from independent differentiation. A good practice for the use of iPSCs in research and in particular for modelling genetic disorders, has been recently published by the ISSCR [https://www.isscr.org/standards-document].\nWe thank the Reviewer for highlighting this important point regarding statistical power and the reproducibility of our results across batches and lines. To address this, we have now provided color-coded graphs where organoids are annotated by their cell line of origin. These visualizations demonstrate that multiple replicates and cell lines were analyzed throughout the study, with negligible inter-line effects observed.\nAs part of this revision, we increased the number of replicates and batches for each cell line to further strengthen the robustness of our analyses. These updates are detailed in the newly generated Supplementary Table 1 and are reflected across all relevant figures in the revised manuscript.\nComment 3\nIt is not clear what is the rational for the scRNA-seq annotation. SUPPLEMENTARY TABLE 4 - List of the top 50 genes… reports main genes in each cluster, but it is no provided a clear indication of which genes have been considered specific to each cellular subtype and used for the annotation. Despite this, the authors identify, for instance, four different groups of motor neurons, but is not clear what drove this specification. More detailed should be provided. Moreover, how the author scRNA-seq data set aligns to other similar analysis in SCOs [PMID: 35347276]?\nWe agree with the Reviewer that the rationale for our previous annotation was not extensively described in the main text and was only briefly mentioned in the Materials and Methods section. For the annotation, we used marker genes and confirmed their cell type specificity using the human spinal cord atlas published by Rayon et al., (2021) as a reference (PMID: 34351410).\nWe wish to thank the Reviewer for their valuable suggestion, which we have now analyzed and incorporated into our comparison. Specifically, we have included a comprehensive panel listing all the markers from the reference cited by the Reviewer, as well as the most important genes used to annotate the clusters. Furthermore, we expanded our list of annotated genes to 300, which is now provided in the revised Supplementary Table 4.\nComment 4\nMost of the immunofluorescence images are below an acceptable quality and fail to properly represent quantitative data or prove co-localization of protein expression, thus hampering proper characterization. For instance: 1) SOX2 staining in Figure 2a SMA panel seems to show increased positive cells in the patients compared to control, or unspecific staining; 2) Figure 2c does not show an increased TUJ1 positivity in the SMA patient SCOs; 3) in Figure 3d is not clear what has been measured to quantify the neurites length, neither proper information is given in the figure legends; 4) Extended Figure 5a is not proving any positivity for TUNEL analysis, while it mostly shows unspecific and spatial restricted signal, which might lead to bias in the interpretation (how quantification has been taken?); Extended Figure 9c immunofluorescence for GAD65 is not really showing believable neurons, and control and SMA images seems at different magnification.\nWe thank the Reviewer for raising these important concerns, and we apologize for the previously low-quality images, which resulted from erroneous file compression during the submission process. To address these issues, we have reorganized and replaced the affected figures with higher-quality images that include appropriate magnifications to enhance clarity and accurately represent the phenotypes.\nSOX2 and TUJ1 Staining (Fig 2a, b): we have replaced these images with higher-quality, more representative versions that clearly illustrate the differences in SOX2-positive and TUJ1-positive cells between SMA and control SCOs. These improvements significantly reduce the likelihood of misinterpretation and better convey the findings described in our study.\nNeurite Length Quantification (Fig. 3h): we have clarified the method used to quantify neurite length in the revised manuscript methods.\nTUNEL Staining (Fig. 5d and Ext. Data Fig. 5a): recognizing the Reviewer’s concerns about the spatially restricted signal and potential bias in interpretation, we have included new representative images that more clearly demonstrate how TUNEL quantification was conducted. This provides a more accurate representation of our analyses and supports the conclusions presented.\nGAD65 Immunofluorescence (Ext. Data Fig. 8b): we replaced the images for GAD65 with higher-quality visuals that distinctly show neuronal populations. To further corroborate our findings, we also performed additional staining for GABA, which is now included in the revised manuscript. These additions provide more robust evidence supporting our interpretations.\nWe believe that these revisions, informed by the Reviewer’s constructive feedback, have significantly improved the quality and clarity of the immunofluorescence data presented in our study. These updated figures and explanations are now included in the revised manuscript.\nComment 5\nSome of the conclusion in the results section do not appear properly justified by the data provided. For instance, page 7 lines 208-217, the solely use of the TUNEL technique does not allow conclusion of cellular death process. Beside the not convincing representative immunofluorescence analysis (see Comment 4), other analysis should have been provided to identify the population undergoing cell death (motor neurons, progenitors, other neurons?). Moreover, Page 9 lines 283-285, the authors stated that transcriptional alteration in SMA does not relate to cell death. It is not clear if neurodegeneration occurs and, if so, at what stage of SCOs maturation.\nWe appreciate the Reviewer’s suggestion to conduct additional analyses to confirm cell death processes. While we attempted supplementary staining with markers such as caspase-3, these efforts unfortunately did not yield reliable results. Additionally, we encountered technical limitations commonly associated with three-dimensional organoid systems due to the presence of the extracellular matrix. These limitations hindered our attempts to perform double staining of TUNEL with other markers, as the harsh conditions required for the TUNEL protocol interfered with other staining procedures. We have acknowledged these limitations in the manuscript and have tempered our conclusions regarding cell death accordingly.\nTo strengthen our findings, we leveraged scRNA-seq data to investigate the enrichment of apoptotic genes. Our analyses revealed that the motor neuron cluster exhibited the highest enrichment for apoptotic gene ontology terms among the relevant clusters (Ext. Data Fig. 5c). This finding highlights the specific vulnerability of motor neurons to apoptotic processes, even if indirectly.\nComment 6\nHow the increased annotated groups ‘progenitors and neurons’ observed in scRNA-seq analysis (Figure 1e and h, Figure 2j and Extended Figure 5e-f) fit with the data reporting no difference in the SOX2 positive cells and decreased TUJ1 positivity in 2 months old SMA SCOs?\nThe Reviewer raises an important point, and we thank them for highlighting this. To clarify, we increased the number of replicates for the analyses and conducted a longitudinal study to strengthen the findings and ensure consistency.\nIn CTRL SCOs, we observed a clear reduction in SOX2-expressing neural progenitors and a corresponding increase in the post-mitotic neuronal marker TUJ1 between 1 and 2 months. In contrast, SMA SCOs exhibited a less pronounced reduction in SOX2 and no significant increase in TUJ1-positive cells over the same period (revised Fig. 2a-d). This indicates a developmental alteration in SMA SCOs.\nIn the scRNA-seq analysis of the 2-month dataset, the trajectory revealed two primary branches. One branch contained progenitors in distinct proliferative states and committed progenitors of the floor plate, while the other branch comprised neural progenitors progressing toward differentiated neurons (revised Fig. 2e-g). SMA SCOs showed a markedly different distribution of cell states compared to CTRL SCOs, with an increased representation of cells at the starting and early segments of the developmental trajectory, indicative of an intermediate stage of development. This altered trajectory was accompanied by a robust and statistically significant reduction in cells reaching the endpoint of the trajectory, which represents maturing neuronal subtypes.\nAltogether, these findings reflect the developmental delay and altered trajectory in SMA SCOs, where progenitors remain abundant, but progression to terminally differentiated neurons is impaired. We believe these integrated analyses provide a clearer picture of the phenotypic differences in SMA SCOs and have clarified this in the revised manuscript.\nComment 7\nPage 13 line 398 the conclusion that unconjugated MO does not rescue phenotype due to increased organoids size is not demonstrated. Testing in smaller organoids should be provided. Moreover, this set of experiments should have included a scrambled oligonucleotide to side to untreated.\nIn response to the Reviewer’s concern regarding the impact of organoid size on phenotype rescue, we confirm that organoids of similar sizes were used throughout molecular profiling experiments to minimize size-related variability. The Reviewer raises an important point regarding the biodistribution of the treatment: to address this, we conjugated morpholino oligonucleotide (MO) with biotin, enabling us to assess its perfusion (Ext. Data Fig. 10). Our analyses revealed effective MO distribution reaching the center of medium-to-large organoids (approximately 3.5 mm in diameter), suggesting adequate delivery of the treatment in these experiments.\nWhile we did not include a scrambled MO in the specific phenotypic assays due to the large quantity of MO required, we did utilize a scrambled MO in our molecular comparisons and splicing analyses (Ext. Data Fig. 10-11). This provided a stringent control framework for evaluating the specificity of MO-mediated effects, strengthening the conclusions of our study. We have clarified these points in the revised manuscript to address the Reviewer’s concerns.\nReviewer #2 (Remarks to the Author):\nIn this study, Faravelli et al compared human SMA type 1 “spinal cord organoids” and “cerebral organoids” from multiple donors. Single-cell RNA-seq revealed changes in the gene expression in various cell types, not only in motor neurons. These included neural progenitors and showed pathological changes in neuronal differentiation. Multi-electrode array analysis showed abnormalities in the activity of both SMA spinal and cerebral organoids. This suggests that SMA pathogenesis could be widespread throughout the central nervous system. The use of antisense oligonucleotides targeting SMN expression in the early stages of organoid development showed a rescue of both morphological and functional deficits in SMA spinal cord organoids. Erroneous splicing in SMN1-target mRNAs was corrected by early treatment.\nOverall the manuscript provides important insights into early pathogenesis mechanisms of SMA, which could not be easily assessed without using human iPS cell-derived neural organoids. The comparison between “spinal cord” and “cerebral” organoids highlighting similarities and differences in disease-induced alterations, as well as the demonstration of treatment mechanisms at cellular and molecular levels, are indeed novel valuable contributions to the field. The suggestions to strengthen the study design and the depth of the analysis were valuable and have improved our manuscript. Below, we detail our point-by-point response to each suggestion, and we believe we have comprehensively addressed the Reviewer’s concerns, providing further support for our results, and highlighting the overall value of the study.\nComment\nThe “spinal cord organoids” are characterized by immnunostaining and scRNAseq. Please compare percent of key cell types (motorneurons, inhibitory neurons, excitatory neurons, progenitors, glia etc) observed by immnunostaning and scRNAseq so that the cellular composition of the organoids. Comparison of the cellular composition between spinal cord organoids and cerebral organoids is critical as basis for the overall study. Please include HB9 for identification of motor neurons.\nWe wish to thank the Reviewer for their kind remarks regarding the significance of our work in the field of SMA. In response to their comments, we have conducted additional analyses using both immunostaining and scRNA-seq data to provide a comprehensive characterization of the cellular composition of spinal cord organoids and facilitate a robust comparison with cerebral organoids.\nTo specifically address the identification of motor neurons, we included HB9 immunostaining using a customized and validated antibody obtained in collaboration with the Motor Neuron Center at Columbia University. Additionally, we performed longitudinal immunostaining for SOX2 (progenitor marker) and TUJ1 (neuronal marker). To improve the robustness of these analyses, we increased the number of replicates and provided novel high-resolution immunostaining panels in the revised manuscript. These updates further highlight key differences between CTRL and SMA spinal cord organoids. Specifically, CTRL organoids exhibited a clear reduction in SOX2-expressing neural progenitors and an increase in TUJ1-positive post-mitotic neurons between 1 and 2 months, while SMA organoids showed a less pronounced reduction in SOX2 levels and no significant increase in TUJ1-expressing cells, reflecting a developmental delay.\nFor cerebral organoids, we specifically assessed whether neural progenitors are affected in SMA by quantifying SOX2+ cells. Our results revealed no significant differences in SOX2+ cell populations between CTRL and SMA cerebral organoids. This finding supports a milder phenotype in the brain compared to the spinal cord, consistent with the known pathogenesis of SMA, where the spinal cord is typically more severely impacted.\nScRNA-seq analysis further validated these findings by revealing distinct cellular trajectories in the 2-month dataset. Cells in CTRL spinal cord organoids split into two primary branches: one comprising progenitors in different proliferative states and committed progenitors of the floor plate, and the other comprising progenitors differentiating into mature neuronal subtypes. In SMA spinal cord organoids, the distribution of cell states was significantly altered, with an increased representation of cells at earlier developmental stages and a statistically significant reduction in cells reaching the endpoint of the trajectory, corresponding to mature neurons (revised Fig. 2f-g).\nTo ensure robust comparisons of cellular composition, we reanalyzed the scRNA-seq data using the latest computational pipelines, allowing for a more accurate characterization of transcriptional differences. This included a detailed analysis of unmatched SMA cells and a partitioning of the scRNA-seq dataset to examine motor neuron lineages across pseudotime and differentiation stages. These additional insights, now included in the revised manuscript (new Fig. 3), strengthen the foundation of our study and provide a more comprehensive understanding of the cellular differences between SMA and CTRL organoids.\nRetinoic acid-induced ventralization could reduce other types of cells in the organoids. Please discuss how retinoid acid-mediated differentiation was controlled in the spinal organoids. Figure 4a indicates SAG and RA are always added to the culture media. Was this intended to maximize motor neuron differentiation and maintenance?\nTo achieve a spinal organoid model with a high proportion of motor neurons, we employed a combination of retinoic acid (RA) and Smoothened Agonist (SAG). This approach was specifically designed to impart a rostral spinal cord identity to the organoids while simultaneously promoting ventralization, which is essential for motor neuron specification. The consistent addition of RA and SAG to the culture media was intended to maximize motor neuron differentiation and maintenance.\nWhile this protocol prioritizes motor neuron production, it also supports the differentiation of spinal cord interneurons, providing a more comprehensive spinal cord-like cellular diversity. To mitigate potential over-ventralization effects, we carefully optimized the concentrations and timing of RA and SAG addition to achieve a balanced cellular composition.\nOur approach aligns with the conditions described in Andersen et al., 2020 (PMID: 33333020), particularly those outlined in conditions 11 and 12, where similar concentrations of RA and SAG were used to drive motor neuron differentiation while preserving cellular diversity within spinal organoids.\nFigure 3d indicated reduction of axon length in SMA neurons from spinal cord organoids. Was this similar in neurons from cerebral organoids?\nWe thank the Reviewer for this insightful question. While we were unable to assess axon length in neurons derived from cerebral organoids due to technical limitations, we instead focused on the assessment of neural progenitors in cerebral organoids to provide a comparative phenotype. This allowed us to explore potential differences between spinal cord and cerebral organoids in the context of SMA.\nPlease explain the y-axis of Figure 4d in more detail. Was it “increased” percentage from the base line? Was it measured right after adding glutamate directly onto the organoids? Please also provide representative traces and raster plots. Also was Extended Figure 10i analyzed at similar time point with Figure 4d? Representative traces should be presented to know temporal changes. as active number of electrodes different between SMA, control organoids, and MO-treated SMA organoids?\nWe thank the Reviewer for these important comments, which allowed us to further refine and enhance the robustness of our functional analyses, a key point of novelty in this manuscript. The y-axis of Fig. 4d represents the percentage change in basal firing frequency. This was calculated by comparing the average firing frequency in the last 10 minutes before glutamate addition with the interval between 5-15 minutes after glutamate addition. This information has now been clearly specified in the Methods section.\nTo provide a more comprehensive view, we have added raster plots for the four conditions (CTRL and SMA, before and after glutamate) to the panel, alongside representative traces. These additions offer better visualization of temporal changes in neuronal activity. Importantly, the number of electrodes considered for the analysis was consistent across all three groups (CTRL, SMA, and MO-treated SMA organoids), and this has been explicitly noted in the Methods.\nRegarding the timing of the analyses, we confirm that Ext. Data Fig. 9f (previously named Ext. Fig. 10i) was assessed at a similar time point as Fig. 4c (previously named Fig. 4d), ensuring comparability. Additionally, we have increased the number of replicates for the functional analyses and included a color-coded assignment for each genetic background to increase clarity and reproducibility.\nReviewer #3 (Remarks to the Author):\nIn this study, the authors use control and spinal muscular atrophy (SMA) spinal cord and cerebral organoids to examine the evolution of gene expression abnormalities over developmental time using single cell sequencing and to assess changes in pre-mRNA splicing patterns after treated with a splice switching antisense oligonucleotide. Emerging data indicate that SMA may start in utero with developmental impairments that are associated with vulnerability to rapid degeneration. Understanding the molecular events underlying these developmental and degenerative events are key for understanding how to optimize therapy.\nThe major findings of this study are 1) SMA spinal cord organoids may be a useful model of human SMA in vitro 2) there are histological and gene expression signatures suggesting very early developmental slowing involving neuronal and nonneuronal cells and 3) a small proportion of abnormal splicing events improve with ASO treatment. Important strengths of the paper are the effort to develop a human SMA spinal organoid model as prior attempts to look at 2D iPSC derived MNs have not clearly revealed disease relevant pathologies. Further the detailed sequencing of human cells has the potential to reveal new insights into disease pathogenesis. Unfortunately, in its current form, there are several experimental limitations (see below. Although the observation of developmental slowing is interesting, it is not clear whether these findings are relevant for the human disease and there is no mechanistic investigation of the identified gene expression changes to validate their importance.\nWe are grateful to Reviewer #3 for recognizing the significant strengths of our work and its potential implications for advancing the understanding of SMA pathology. We also deeply value the constructive suggestions to refine our study design, which have enabled us to draw more robust and accurate conclusions. In response to their feedback, we have thoroughly addressed each concern and believe that these improvements have substantially enhanced the rigor and impact of our study.\nSpecific comments:\n1) Several aspects of the initial CNTL and SMA organoid characterizations need to be strengthed. In an extended table, 6 CNTL and 7 SMA lines are listed ranging in age from newborn to 34 years for CNTL and fetal to 2 years for SMA. Only some of these lines are actually used in the paper and for most experiments only 1 to 2 lines each are used. It must be clarified which lines are used for which experiments and whether the differences in subject age play an important role in the behavior of the iPSCs. Further, a minium of 3 lines each (with duplicate derivations) should be used for most experiments. No information regarding SMN1/SMN2 copy number or SMA type is provided. There is no baseline assessment of SMN mRNA or protein levels amongst the different lines over differentiation time (the authors make the point in the intro that SMN may decrease in whole human tissues over developmental time). Although key markers of iPSC differentiation are quantified over differentiation time in several CNTL lines, this quantitative data are not provided for the SMA lines (Ext data figure 1).\nIn the current version of the manuscript, we addressed all the points raised here by the Reviewer, further strengthening the robustness of our results.\nWe increased the number of lines for the experiments and made clearer their allocation in the different analyses (we provide a novel Supplementary Table 1 where all experiments are clarified, and we maintained a color code for each line throughout the entire manuscript). This implied the generation of new batches of both spinal cord and brain organoids from CTRL and SMA lines. We have now provided color-coded graphs where organoids have been annotated by the cell line of origin. As it can be observed, several replicates and multiple cell lines have been analyzed throughout all the analyses with negligible inter-line effect. For this revision, we increased the number of replicates and batches for each line, as highlighted both in the newly generated Supplementary Table 1 and across all figures in the manuscript.\nFor SMN1/2 copy number, we have now provided an extensive characterization of all the iPSC lines generated de novo for this work, including evaluation of 1) morphology, 2) genome integrity, and 3) self-renewal markers (newly generated Ext. Data Fig. 1). We used the same parameters on representative commercial lines, as internal controls. Regarding the commercial and previously published lines, we provided more detailed information about their source and features.\nFinally, we extensively characterized the SMN molecular phenotype of the iPSC lines (such as qPCR analyses on the copies of SMN and the evaluation of the expression of the ratio FL/D7 SMN and SMN expression levels over time in culture, Ext. Data Fig. 1d-f) and provided further details of the clinical phenotypes of each donor/patient (Ext. Data Fig. 1h).\nKey markers of iPSC differentiation have been quantified over in SMA lines as well in the revised version of the manuscript (Ext. Data Fig. 1g).\n2) During the initial characterization of the SMA organoids, the authors note a transcription profile that correlates with Carnegie Stage CS17. They refer to this as a human fetal stage, but in fact this classifcation refers to human embryo staging (first 10 weeks of gestation are embyro and 10-40 weeks are fetus). This is an important distinction as this implies that the organoids are VERY IMMATURE. Although studies of human type I SMA tissues indicate MN developmental abnormalities at mid-late fetal stages, I am not aware of abnormalities dating to the embyro. How do the authors conceptualize the developmental processes in these organoids relative to human SMA development.\nWe thank the Reviewer for raising this important point and apologize for any lack of clarity in the original submission. In the revised manuscript, we updated our references for the endogenous tissue (Ext. Data Fig. 3c) to include a study that performed single-cell RNA sequencing of human spinal cord cells across multiple time points during embryonic and fetal stages. Our dataset showed the strongest alignment with early developmental stages (first trimester), and we have amended our text accordingly. The transcriptional alterations observed in progenitors, which represent a significant novelty, align with the developmental nature of the model and provide valuable insights into the early events of human spinal cord neurogenesis.\n3) There seems to be a disconnect between the number of SOX2+ neural progenitors at 1 month and 2 month (CNTL and SMA are equivalent at 2 months) compared to the number of apoptotic cells at these same time points. The authors need to provide the statistical comparison between CNTL and SMA in number of apoptotic cells at 1 mo and 2 months. What cell types are actually dying at 2 months? Do MNs actually undergo apoptotic cells death or is this another cell type?\nWe thank the Reviewer for the opportunity to clarify these points. While CTRL SCOs exhibited a clear reduction in SOX2-expressing neural progenitors and an increase in the post-mitotic neuronal marker TUJ1 from 1 to 2 months, SMA SCOs demonstrated a less pronounced reduction in SOX2 levels and no significant increase in the proportion of TUJ1-expressing cells. Single-cell trajectory analysis of the 2-month dataset revealed two major branches: one containing progenitors in distinct states of proliferative activity and committed progenitors of the floor plate, and another comprising neural progenitors progressing toward differentiated neurons (revised Fig. 2f-g). SMA SCOs displayed a strikingly altered distribution of cell states, with an increased representation of cells at the starting and early segments of the developmental trajectory—indicative of an intermediate stage of development. This altered trajectory was accompanied by a robust and statistically significant reduction in cells at the endpoint of the trajectory, corresponding to maturing neuronal subtypes.\nWe appreciate the Reviewer’s suggestion to conduct further analyses to confirm cell death processes. Although we attempted supplementary staining using markers such as caspase-3, these efforts did not yield reliable results. Additionally, technical limitations—commonly encountered in 3D organoid models due to the extracellular matrix— made it challenging to perform double staining of TUNEL with other markers. The harsh conditions required for the TUNEL protocol interfered with other staining procedures. We have acknowledged this limitation in the manuscript and tempered our conclusions regarding cell death accordingly.\nTo address this challenge, we leveraged single-cell data to investigate the enrichment of apoptotic genes. Our analyses revealed that the motor neuron cluster exhibited the highest enrichment for apoptotic gene ontology terms among the relevant clusters (Ext. Data Fig. 5c). This observation, combined with the altered developmental trajectory in SMA SCOs, suggests that the vulnerability of motor neurons to apoptosis could be linked to their disrupted progression through differentiation.\n4) The authors note that there are many gene expression abnormalities between SMA and CNTL, but identify only one gene (NRXN2) that has been previously reported to be altered in SMA tissue. A formal comparison to other data sets might help strengthen the relevance of these gene expression changes.\nAfter reanalyzing our data using the latest scRNA-seq pipeline, we obtained a cleaner and more robust dataset. We performed a de novo comparison of bulk RNA-seq data from in vitro differentiated SMA and CTRL human motor neurons (PMID: 30649277) with the scRNA-seq differentially expressed genes (DEGs) identified in motor neurons. This updated analysis yielded an expanded list of previously reported genes, now totaling 24. These include CAMK2B, GRIA1, GRM4, NRXN1, PCDH7, PCDH10, PRKCG, PSD2, SCN2A, and SNAP91, many of which are associated with neuronal function and ion channel activity.\n5) SMI32 is used to define axon length. More thorough analysis to prove these are MNs with MN specific markers and stains that would specifically identify axons vs. dendrites. As is perhaps more accurate to call neurites?\nWe agree with the Reviewer and have amended the text and figure legend accordingly.\n6) Extended data 9 includes only 1 CNTL line (see comment about n above).\nThe Reviewer is correct in pointing out this limitation. In the current version of the manuscript, we have highlighted the contribution of each cell line to the respective analyses. While we were unable to increase replicates across all experiments for the brain organoids due to cost and time constraints, we prioritized increasing the number of lines, batches, and replicates for the key experiments. This ensured alignment with the approach used for the spinal cord organoids. Specifically, for the functional analyses, this effort resulted in the production and analysis of additional organoids for the MEA glutamate assay (Fig. 4h, i).\n7) There is inadequate characterization of the cerebral organoids with no quantification of the basic markers over differentation time.\nBrain organoids are inherently heterogeneous, and we specifically selected this model to provide an unbiased assessment of the potential impact of SMA on the CNS. This approach allowed us to investigate the broad effects of SMA without limiting our analysis to specific brain regions, thereby supporting the overarching hypothesis of our study. While this heterogeneity adds value by capturing a more comprehensive view of SMA pathology, it also presents challenges in organoid-to-organoid reproducibility.\nTo address this, we focused our quantitative analyses on the general progenitor marker SOX2, assessing its expression at both the protein and RNA levels. This decision aligns with our findings in spinal cord organoids, which represent a central focus of the manuscript. Furthermore, we characterized the emergence of specific neural markers at selected time points to provide additional insights into the differentiation timeline and cellular composition of the organoids.\nVery similar histological and electrophysiological phentoypes are reported in these organoids as reported in the SC organoids. Although the brain may be affected in some very severely affected SMA patients, it is much less severely affected than the spinal cord. It would have been much more impactful to see divergent phenotypes between these two organoid systems-why is this not seen?\nWe appreciate the Reviewer’s observation regarding the phenotypic similarities between the cerebral and spinal cord organoids and the suggestion that divergent phenotypes would be more impactful. To address this, we expanded our analysis on cerebral organoids, focusing on the characterization of fundamental markers and, in particular, progenitor cell phenotypes.\nSpecifically, we assessed whether neural progenitors are affected in SMA cerebral organoids by quantifying SOX2+ cells. Our results revealed no significant differences in SOX2+ cell populations between CTRL and SMA cerebral organoids. This finding supports the Reviewer’s suggestion of a milder phenotype in the brain compared to the spinal cord, consistent with the known pathogenesis of SMA, where the spinal cord is typically more severely impacted.\n9) The treatment experiments as currently presented are not sufficiently rigorous. CNTL lines need to be run in parallel to assess nonspecific changes arising from treatment and as a reference point for SMN expression. A scrambled ASO is also needed. The targeting of the ASO to cells in these organoids need to directly assessed. How many cells and what cell types actually took up the ASO? Were the modest improvements in splicing patterns due to inadequate drug distribution or splicing changes no longer reversible by SMN induction? Those genes that can not reverse their splicing pattern even after full SMN repletion would be very interesting for the SMA field to identify particularly if they are relevant to disease manifestations in patients.\nWe thank the Reviewer for highlighting this important aspect. During the revisions, we conducted additional batches of SCOs and implemented a robust experimental design to address the concerns raised. Treatments with r6-MO, scrambled r6-MO, and collections of untreated SMA SCOs were performed in parallel, allowing us to assess nonspecific changes and establish an appropriate reference point, as suggested by the Reviewer. The use of scrambled r6-MO as a control provided a stringent analytical framework, ensuring that the observed changes were specific to r6-MO treatment. This approach enabled the identification of a robust set of key genes whose splicing was reverted upon r6-MO treatment (revised Fig. 6). Importantly, genes that did not exhibit splicing reversion were linked to broader pathophysiological processes, suggesting systemic involvement beyond spinal cord pathology (new Suppl. Table 10).\nTo address the distribution of r6-MO within the organoids, we utilized a biotinylated version of r6-MO and traced its localization using biotin staining (Ext. Data Fig. 10d). The staining revealed widespread distribution of r6-MO, spanning both the periphery and the center of the organoids. This widespread distribution, coupled with the recovery of SMN levels (Ext. Data Figure 10e), suggests that the modest splicing improvements observed are likely related to the irreversible splicing state of certain genes. Interestingly, among the genes associated with reverted splicing events, we identified histone-modifying enzymes, which could potentially play a role in partially restoring wild-type gene expression."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Remarks to the authors\nThe submitted manuscript from Faravelli, Rinchetti, Tambalo and colleagues provides a in depth study of SMA underlying disease mechanisms using SMA patient-derived iPSCs to generate spinal cord and cerebral organoids. The team has also leveraged on their established patient-derived spinal cord model to test ASOs based therapeutic strategies, which could rescue both developmental and functional pathophysiological phenotypes.\nThe team has previously submitted their work to Nature Communications in 2023. Since then, the group has significantly improved the analysis in support of their observations and conclusions, further incrementing the importance of this work. As already discussed, this work importance is supported by several novel aspects:\n• Single cell RNA sequencing analysis provides an in-depth characterization of control and SMA derived SCOs indicating developmental dysregulation.\n• Generation of SMA cerebral organoids and the study of functional cortical-related dysfunction.\n• Electrophysiological analysis is the first of its kind in SMA-derived SCOs and cerebral organoids.\n• Evaluation of antisense oligonucleotides efficiency in patient-derived SCOs.\nMoreover, the presented revised study expands the mechanistic analysis underlying SMA pathophysiology, investigating downstream splicing defects and therapeutic rescue.\nThe team has extensively and adequately addressed all remarks provided from this reviewer in the first revision. This has increased the rigor of the scientific approach and significance of the results, which will provide great benefit in the field. I therefore support the publication of this manuscript.\nI would like to still mention few minor points, which might require attention or, if addressed, could improve some aspects of the analysis.\nMinor points:\nMinor point 1\nExtended figure 3: figure legend descriptions for sections “c” and “d” have been inverted. Can the author please revise this?\nMinor point 2\nFigure 2c: I would like to suggest, if possible, to evaluate the ratio of SOX2 reduction between 1 and 2 months in CTRL SCOs and SMA SCOs. This could emphasize the relative lower longitudinal reduction of SOX2 positive cells observed in SMA SCOs. Maybe the author could use grouped stack charts or line chart to better visualize retention of neuronal stem cell state in SMA SCOs.\nMinor point 3\nLine 197: the sentence “while the altered composition with enriched progenitors and neurons suggests changes in early developmental programs in SMA tissue” could be strengthen by performing statistical analysis of SOX2 positive cells at 2 months between CTRL SCOs and SMA SCOs. This will further support the retention of neural stem cells population and altered developmental programs in SMA. Also, I am not sure if the authors wanted to indicate “enriched neurons” as this seems in contrast with their results.", "reviewer_2": "Most of the comments have been addressed.\nIt would be reasonable to explain more about the comment \"we were unable to assess axon length in neurons derived from cerebral organoids due to technical limitations\" to address the question, as the progenitor phenotype is not comparable to the axonal phenotypes.", "reviewer_3": "The authors are to be commended for their efforts to improve this manuscript. There are extensive data reported and the conclusion that there are early impairments of differentiation of SMA organoids is more convincing. I still have some concerns:\n1. There has been a good effort to better characterize the organoids and extended figure 1 and 2 includes some data on 5 CNTLS and 5 SMA. Nonetheless, the number of lines used for the different experiments continues to vary throughout the manuscript and at times is not a sufficient number (n=1-3). There remains no quantification of stem cell or neurodevelopmental markers except for 3 genes in 2 CNTL and 1 SMA line.\nThe baseline characterization of SMN1/SMN2 gene copy number and SMN protein level remains confusing/weak. IN Ext data FIgure 1e, there is SMN1 and SMN2 qPCR, but is this gene copy number or mRNA? Either way, why would there be detectable SMN1 in the SMA line and why is this not shown for all lines? Ext Data Figure 1F shows mRNA levels I believe, but only display the data as a ratio. It would be ideal to show the mRNA data as absolute values for each FL and delta 7 SMN2 transcripts. If I am reading the manuscript right, there is not western blot data for SMN protein levels in the different CNTL and SMA lines over the organoid differentation period. SMA is caused by reduced SMN PROTEIN expression. It is essential to establish SMN protein levels during th differentation period. The authors discuss the importance of decreasing SMN protein levels during development in SMA in the introduction.\nOf note, in extended figure 1d, all the data points for mesoderm, ectoderm, endoderm are at zero-is this a mistake?\n2. For Figure 1, single cell data are generated for 2 CNTL and 2 SMA lines. This would ideally be at least 3 individual lines. The figure legend should also make clear which lines were used for this experiment (as has been done for other figure legends). Another point that needs to be discussed as a potential drawback in the discussion is that the single cell analysis is done at a single time point. Trajectory analysis (done later in manuscript) with one time point is not as strong as assessment of more than one time point. Can the authors be certain of developmental impairments based on a single timepoint?\n3. Figure 2, high content image analysis is done, but the statistical analysis between CNTL and SMA at each time point should be shown in the figure not just the comparison between 1 and 2 mos within genotype. The SMN protein changes over this interval would be ideal when interpreting these data (See point 1 above). For the apoptosis data, are the TUNEL+ cells definitely neurons? Is it possible that SMA is slower to go through nml developmental pruning?\n4. Figure 3, the further trajectory analysis of specific MN clusters looks like a nice addition. An enrichment of MNs along a single branch is observed, but the meaning and implications of this are not discussed in results or discussion. This analysis is based on a single time point and the potential weakness of not assessing more than one time point should be acknowledged in the discussion.\n5. I have some concerns about the calcium imaging and am not sure these data are necessary. There is no attempt to assess calcium influx in individual cells and really need to esablish changes in neurons. The electrophysiology data are stronger and make the same point.\n6. I appreciate the efforts to improve the ASO treatment experiment. There has been some effort to assess biodistribution with biotin, but all that is included in the manuscript is a single image of biotin ICH that still shown biotin staining predominantly at the periphery. The data supporting efficicent biodistribution should be quantified. It would be ideal to show uptake of ASOs in the motor neurons specifically given this is the most important target cell. Why were 3 doses of ASO required over a fairly short interval? How was the treatment regimen determined?\nI am also concerned that there is no inclusion of treated CNTLs in these experiments. This is particularly critical for interpretation of the splicing changes and whether improvements can be definitely attributed to changes in SMN expression. There also seems to be a disconnect between SMN protein induction, which appears fairly modest, and the change in ratio FL/d7 SMN mRNA that ranges from a ratio of 30 to 400? Again seeing the individual FL and d7 mRNA levels would be clarifying.\n7. Another important point is that this is not the first published study to report on SMA organoids. See Cell Rep Med 2024 Aug 20;5(8):101659. Isogenic patient-derived organoids reveal early neurodevelopmental defects in spinal muscular atrophy initiation. Tobias Grass et al. I don't see that this paper has been cited or that the findings in the two papers are compared and discussed. This previous publication does potentially lessen the novelty.\nMinor:\n1) Title may be a bit overstated with rescue of developmental alternations\n2) SMA does not show an absence of SMN protein-see line 149 and elsewhere. Better to state SMN is reduced.\n3) From abstract, I am not sure I am convinced that the oligo \"restored\" SMN-would need to show a WB comparing CNTL to SMA treated to prove this.\n4) Generic drug nusinersen is lower case and acts on SMN2 pre-RNAs not of SMN2 (italic implying gene).\n5) What specific neurofilament was analyzed is unclear. There is mention of SMI32 in text (which assesses both medium and heavy unphosph NF), but the graphic shows NFL and there is NFH in the text. This needs to be clarified", "reviewer_4": "Review of the single cell analysis: The authors perform single cell analysis in control and SMA iPSC derived spinal cord organoids to study early developmental defects in SMA. The authors demonstrate that gene expression in neuronal progenitors and postmitotic neurons is altered in SMA, suggesting that SMA-related defects start to emerge at very early developmental stages. The authors also note that the developmental trajectory and regulation of motor neurons is altered in SMA. These findings are novel and interesting, however, shortcomings in data analysis and interpretation were noted and are outlined below.\nSimilarity between single cell sequencing replicates\nscRNAseq were performed independently for 2 control and 2 SMA lines. It is stated that data from 27,114 cells were obtained but it is not mentioned how many cells form each genotype and cell line passed the quality control thresholds. In addition, there is no mention of how similar the single cell profiles of the 2 control/SMA lines were to each other . Separate UMAP plots for control and SMA cells are shown in Ext Data Fig. 3a, but within these, it would be important to see the distribution of cells from each of the 2 lines. This is important for understanding line to line variability and ascertain that the differences reported are consistently present in both SMA lines.\nAssigning identity to clusters\nIt is unclear how cells in Fig.1c UMAP are assigned MN identity. Ext. data fig 3e shows module score enrichment for some of the cell types identified but not for MNs. A quick look at the top 300 genes for each cluster in Supplementary Table 4 does not show enrichment of key MN markers in clusters identified as MNs. Specifically, Isl1, Isl2, Mnx1, Aldh1a2, Slc10a4, Arhgap36, which were identified as MN markers in human spinal cord single cell sequencing [PMCID: PMC8353162] seem to be absent in the MN clusters in supplementary table 4. How do the authors explain this? Please include a clear description for how MN identity was assigned to these cell types.\nIt is understood that cells in organoids will not have the same gene expression pattern as in vivo cells, however it would be useful to know how similar/different the cell types in organoids are to their in vivo counterparts by performing correlation analysis between identified in vitro cell types and in vivo cell types from [PMCID: PMC8353162 and/or PMCID: PMC8567249].\nAre MN subclusters differentially affected by SMA?\nMNs are treated as one group in the data when quantifying differences in cell population (Fig. 1g). However there seem to be two distinct population of ‘MN’, one expressing Phox2a/b, markers associated with visceral or cranial MNs and another expressing Isl1/Mnx1 which are markers of skeletal MNs including limb innervating. First it would be important to clarify if these two represent different known subtypes of MNs based on other genes expressed in these cells. Second, it would be important to quantify these populations separately as they seem to behave differently in control vs. SMA. This seems important because in Fib1f/g, although an overall reduction in MN population is shown, it looks as if one group of MNs (Phox2+) is reduced in SMN SCOs, while the other (Isl1+, Mnx1+) group is increased. Is this the case (based on quantification of cell numbers in these clusters) and if so, how do the authors explain this?\nExt. Data Figure 6a: The third cluster labeled Isl1/2, Mnx1 high does not seem to express MNx1 based on 6c but does express high levels of Phox2b- is this mislabeled? There are also three clusters labeled as Isl1/2, Mnx1 in Fig. 3a- are those correct?\nPseudotime Analysis of whole dataset and MNs\nPseudotime analysis in Fig. 2: The analysis suggests that the MN lineage turns into V2a/b lineage in the wildtype right most branch, this raises questions about the biological relevance of this analysis- should there not be a separate progenitor to V2 lineage branch? How do the authors explain this? (Minor: would be helpful to have colors associated with celltypes shown again in a legend). It would help to briefly include the authors understanding of any limitations in this analysis.\nMN developmental trajectory in Fig. 3: It is unclear if this developmental trajectory analysis is adding new/relevant biological understanding to the study. In the methods it is stated that: “The root was set to the SOX2 expression using scf.tl.root and corresponded to the pMN”. However, it seems that the highest Sox2 activity/expression is in MNs at the top of this tree, while pMNs at the bottom of the tree have lower Sox2 activity (Fig. 3d). How do the authors interpret this analysis?\nThe dendogram does show progression from progenitors on the bottom to MNs on top, but how should we interpret that the branches merge into one specific sub population of postmitotic MNs at the top? Is the analysis suggesting that MNs go through different states and eventually become the type of cell that is found at the top of the trajectory? This is further confusing because the top population of MNs is highly enriched for Sox2 regulon in Fig. 3d. which should be a marker for progenitor cells.\nIt is unclear why the authors do not show the Isl1/Lhx3 regulons in CTRL and SMA (like for Sox2, Phox2, Chd2) as these factors are known drive MN differentiation and gene expression [PMCID: PMC3188742", "reviewer_pmcid": "PMC3820498]? Lhx3 is labeled on the left heatmap but difficult to interpret due to lack of labeling for which columns of heatmap correspond to which branches of the dendogram.\nIt is clear that there are differences between control and SMA analyses in terms of distribution of cells and regulons, but without an understanding of the biological relevance of these dendograms, it is difficult to conclude that these changes are meaningful for MN development.\nResponse (Part A – Whole dataset pseudotime): We thank the reviewer for raising this important point. We fully agree that applying pseudotime trajectory inference to the entire dataset can lead to overconnected structures that may not reflect true lineage relationships. Specifically, the apparent connection between the motor neuron and V2a/b lineages in the original dendrogram tree is not biologically accurate, as these lineages arise from distinct progenitor domains (pMN and p2, respectively). To avoid overinterpretation of such artifacts, we decided to limit the use of pseudotime in the global analysis. Rather than inferring multi-branching trajectories, we focused on the distribution of pseudotime scores across genotypes and cell types, which provides a robust, model-free readout of transcriptional maturation. The updated version of Figure 2 now shows this analysis. As illustrated in Figure 2f–h, SMA organoids exhibit a significant leftward shift in pseudotime scores compared to CTRLs, consistent with a delay in transcriptional maturation. This trend is observed not only in motor neurons, but across multiple cell types including progenitors, pMN, neurons, V2a/b, and pRP populations. These results support the idea that SMA impacts the overall developmental progression of spinal cord cell types, rather than affecting only a specific lineage. We have revised the text and figure legends accordingly, and we thank the reviewer again for encouraging a more cautious and biologically grounded interpretation of the data.\nResponse (Part B – MN pseudotime): We thank the reviewer for these thoughtful comments and for pointing out inconsistencies in the original pseudotime trajectory visualization. We have carefully reviewed the analysis and made several corrections and improvements in response:\nAs the reviewer correctly noted, the original dendrogram contained inconsistencies in color labeling, which may have contributed to confusion regarding cluster identity and trajectory interpretation. We have now corrected these color assignments and added explicit cell type labels to the dendrogram plot to improve clarity.\nIn revisiting the annotation, we realized that a subset of cells originally labeled as intermediate progenitors (INPs) within the motor neuron (MN) lineage more closely resembled neurogenic progenitors (NPs) based on their transcriptional profile and their comparison to the human fetal reference. We therefore reclassified these cells accordingly and excluded them from the MN partition before re-running the downstream analysis. This refinement improved the biological consistency of the trajectory, as the corrected annotation more accurately reflects known stages of motor neuron development.\nWith these updates, it is now evident that the region with the highest SOX2 regulon activity, located at the top of the dendrogram, corresponds to the pMN cluster (Figure 3d-e), in agreement with known in vivo developmental patterns. Interestingly, the persistence of SOX2 activity in downstream states, specifically in the SMA condition, may reflect a delay in motor neuron differentiation, as also observed in the global pseudotime analysis (Figure 2f-h). This interpretation is further supported by our immunofluorescence data (Figure 2a-e), which show a sustained presence of SOX2⁺ cells between DIV30 and DIV60 in SMA organoids, in contrast to the decline observed in CTRL samples. We have updated both the Methods and Results sections to clarify that the dendrogram does not represent a strict lineage tree, but rather a continuum of transcriptional states within the MN lineage, shaped by gradual transitions and regulon dynamics. To facilitate interpretation of the heatmaps, we have added top annotations indicating both the pseudotime score (corresponding to the diffusion map shown in Figure 3b) and the cell type/clusters color codes matching the MN subtype labels in Figure 3a. This visual alignment enables the reader to directly relate regulon activity patterns to both transcriptional progression and cell identity.\nReviewer #4 - Summary of major comments\nIt would be helpful to include more information about number of cells and similarity between single cell sequencing results from the two replicates of control and SMA organoids.\nWe have now fully addressed this concern.\nThe authors should also include a more detailed description of how MN identity is assigned and ideally a comparison of in vitro cells to in vivo MN, V2a, V2b, and progenitors data from human spinal cords.\nWe have now fully addressed this concern.\nWhether the pseudotime/regulon analysis in Fig. 3 reflects meaningful developmental mechanisms is unclear based on the information currently provided. The authors summarize these analyses as showing that the development and regulation of progenitor to MN trajectory is disrupted in SMA- while this may be true, the presented analysis does not make a convincing case.\nWe have now fully addressed this concern.\nImmunostaining performed for Sox2 and TuJ1 elsewhere in the manuscript provides some information about the prevelance of progenitor vs. neurons and their developmental/cell death trajectory, but this does not provide specific information about MN trajectory, for which more MN specific markers such as Olig2 (for pMNs), Isl1, Mnx1, Lhx3 (for MNs) would be more useful.\nWe appreciate the suggestion and recognize the value of further immunostaining analysis. Unfortunately, due to limitations in the remaining material, we were unable to perform additional quantification without initiating a new round of differentiations.\nReviewer #4 - Minor Comments\nFig1j and Fig 3g: Unclear which GO terms are from upregulated OR downregulated genes.", "reviewer_author_response": "RE: Reviewer 1\nReviewer #1 – Minor point 1\nExtended figure 3: figure legend descriptions for sections “c” and “d” have been inverted. Can the author please revise this?\nReviewer #1 – Minor point 2\nFigure 2c: I would like to suggest, if possible, to evaluate the ratio of SOX2 reduction between 1 and 2 months in CTRL SCOs and SMA SCOs. This could emphasize the relative lower longitudinal reduction of SOX2 positive cells observed in SMA SCOs. Maybe the author could use grouped stack charts or line chart to better visualize retention of neuronal stem cell state in SMA SCOs.\nReviewer #1 – Minor point 3\nLine 197: the sentence “while the altered composition with enriched progenitors and neurons suggests changes in early developmental programs in SMA tissue” could be strengthen by performing statistical analysis of SOX2 positive cells at 2 months between CTRL SCOs and SMA SCOs. This will further support the retention of neural stem cells population and altered developmental programs in SMA. Also, I am not sure if the authors wanted to indicate “enriched neurons” as this seems in contrast with their results.\nReviewer #3 – Q1\nThere has been a good effort to better characterize the organoids and extended figure 1 and 2 includes some data on 5 CNTLS and 5 SMA. Nonetheless, the number of lines used for the different experiments continues to vary throughout the manuscript and at times is not a sufficient number (n=1-3). There remains no quantification of stem cell or neurodevelopmental markers except for 3 genes in 2 CNTL and 1 SMA line.\nReviewer #3 – Q2\nFor Figure 1, single cell data are generated for 2 CNTL and 2 SMA lines. This would ideally be at least 3 individual lines. The figure legend should also make clear which lines were used for this experiment (as has been done for other figure legends). Another point that needs to be discussed as a potential drawback in the discussion is that the single cell analysis is done at a single time point. Trajectory analysis (done later in manuscript) with one time point is not as strong as assessment of more than one time point. Can the authors be certain of developmental impairments based on a single timepoint?\nReviewer #3 – Q3\nFigure 2, high content image analysis is done, but the statistical analysis between CNTL and SMA at each time point should be shown in the figure not just the comparison between 1 and 2 mos within genotype. The SMN protein changes over this interval would be ideal when interpreting these data (See point 1 above).\nReviewer #3 – Q4\nFigure 3, the further trajectory analysis of specific MN clusters looks like a nice addition. An enrichment of MNs along a single branch is observed, but the meaning and implications of this are not discussed in results or discussion. This analysis is based on a single time point and the potential weakness of not assessing more than one time point should be acknowledged in the discussion.\nReviewer #3 – Q5\nI have some concerns about the calcium imaging and am not sure these data are necessary. There is no attempt to assess calcium influx in individual cells and really need to esablish changes in neurons. The electrophysiology data are stronger and make the same point.\nReviewer #3 – Q6\nI appreciate the efforts to improve the ASO treatment experiment. There has been some effort to assess biodistribution with biotin, but all that is included in the manuscript is a single image of biotin ICH that still shown biotin staining predominantly at the periphery. The data supporting efficicent biodistribution should be quantified. It would be ideal to show uptake of ASOs in the motor neurons specifically given this is the most important target cell. Why were 3 doses of ASO required over a fairly short interval? How was the treatment regimen determined?\nReviewer #3 – Q7\nAnother important point is that this is not the first published study to report on SMA organoids. See Cell Rep Med 2024 Aug 20;5(8):101659. Isogenic patient-derived organoids reveal early neurodevelopmental defects in spinal muscular atrophy initiation. Tobias Grass et al. I don't see that this paper has been cited or that the findings in the two papers are compared and discussed. This previous publication does potentially lessen the novelty.\nReviewer #3 - Minor:\n1) Title may be a bit overstated with rescue of developmental alternations\n2) SMA does not show an absence of SMN protein-see line 149 and elsewhere. Better to state SMN is reduced.\n3) From abstract, I am not sure I am convinced that the oligo \"restored\" SMN - would need to show a WB comparing CNTL to SMA treated to prove this.\n4) Generic drug nusinersen is lower case and acts on SMN2 pre-RNAs not of SMN2 (italic implying gene).\n5) What specific neurofilament was analyzed is unclear. There is mention of SMI32 in text (which assesses both medium and heavy unphosph NF), but the graphic shows NFL and there is NFH in the text. This needs to be clarified.\nReviewer #4 – Q1: Similarity between single-cell sequencing replicates\nscRNAseq were performed independently for 2 control and 2 SMA lines. It is stated that data from 27,114 cells were obtained but it is not mentioned how many cells form each genotype and cell line passed the quality control thresholds. In addition, there is no mention of how similar the single cell profiles of the 2 control/SMA lines were to each other. Separate UMAP plots for control and SMA cells are shown in Ext Data Fig. 3a, but within these, it would be important to see the distribution of cells from each of the 2 lines. This is important for understanding line to line variability and ascertain that the differences reported are consistently present in both SMA lines.\nReviewer #4 – Q2: Assigning identity to clusters\nIt is unclear how cells in Fig.1c UMAP are assigned MN identity. Ext. data fig 3e shows module score enrichment for some of the cell types identified but not for MNs. A quick look at the top 300 genes for each cluster in Supplementary Table 4 does not show enrichment of key MN markers in clusters identified as MNs. Specifically, Isl1, Isl2, Mnx1, Aldh1a2, Slc10a4, Arhgap36, which were identified as MN markers in human spinal cord single cell sequencing [PMCID: PMC8353162] seem to be absent in the MN clusters in supplementary table 4. How do the authors explain this? Please include a clear description for how MN identity was assigned to these cell types. It is understood that cells in organoids will not have the same gene expression pattern as in vivo cells, however it would be useful to know how similar/different the cell types in organoids are to their in vivo counterparts by performing correlation analysis between identified in vitro cell types and in vivo cell types from [PMCID: PMC8353162 and/or PMCID: PMC8567249].\nReviewer #4 – Q3: Are MN subclusters differentially affected by SMA?\nMNs are treated as one group in the data when quantifying differences in cell population (Fig. 1g). However there seem to be two distinct population of ‘MN’, one expressing Phox2a/b, markers associated with visceral or cranial MNs and another expressing Isl1/Mnx1 which are markers of skeletal MNs including limb innervating. First it would be important to clarify if these two represent different known subtypes of MNs based on other genes expressed in these cells. Second, it would be important to quantify these populations separately as they seem to behave differently in control vs. SMA. This seems important because in Fib1f/g, although an overall reduction in MN population is shown, it looks as if one group of MNs (Phox2+) is reduced in SMN SCOs, while the other (Isl1+, Mnx1+) group is increased. Is this the case (based on quantification of cell numbers in these clusters) and if so, how do the author explain this? Ext. Data Figure 6a: The third cluster labeled Isl1/2, Mnx1 high does not seem to express MNx1 based on 6c but does express high levels of Phox2b- is this mislabeled? There are also three clusters labeled as Isl1/2, Mnx1 in Fig. 3a- are those correct?\nReviewer #4 - Q4: Pseudotime Analysis of whole dataset and MNs\nPseudotime analysis in Fig. 2: The analysis suggests that the MN lineage turns into V2a/b lineage in the wildtype right most branch, this raises questions about the biological relevance of this analysis- should there not be a separate progenitor to V2 lineage branch? How do the authors explain this? (Minor: would be helpful to have colors associated with celltypes shown again in a legend). It would help to briefly include the authors understanding of any limitations in this analysis.\nMN developmental trajectory in Fig. 3: It is unclear if this developmental trajectory analysis is adding new/relevant biological understanding to the study. In the methods it is stated that: “The root was set to the SOX2 expression using scf.tl.root and corresponded to the pMN”. However, it seems that the highest Sox2 activity/expression is in MNs at the top of this tree, while pMNs at the bottom of the tree have lower Sox2 activity (Fig. 3d). How do the authors interpret this analysis?\nThe dendogram does show progression from progenitors on the bottom to MNs on top, but how should we interpret that the branches merge into one specific sub population of postmitotic MNs at the top? Is the analysis suggesting that MNs go through different states and eventually become the type of cell that is found at the top of the trajectory? This is further confusing because the top population of MNs is highly enriched for Sox2 regulon in Fig. 3d. which should be a marker for progenitor cells.\nIt is unclear why the authors do not show the Isl1/Lhx3 regulons in CTRL and SMA (like for Sox2, Phox2, Chd2) as these factors are known drive MN differentiation and gene expression [PMCID: PMC3188742"}, "author_response": ""}, {"round": 3, "reviewer_comments": {"reviewer_4": "Responses to Reviewer 3:\nThe authors have largely addressed these comments.\nRegarding Q3: Adding additional bar plots is not necessary, but it would be useful to add the comparisons between CTRL and SMA in the form of *, ***, or ns in the plot already presented in Figure 2c, 2d.\nResponse to Reviewer 4 (review of the single cell analysis):\nThe authors have carefully considered many of my comments and added extensive new analysis. The additional comparison of single cell data from organoids to in vivo single cell datasets, and the clear subtype labeling of MNs based on marker expression addresses many of my concerns in Q2 and Q3. My concerns in Q4 are also addressed by improvements to the pseudotime/lineage analysis.\nHowever, I’m afraid that my first point (Q1) about how similar single cell profiles within the 2 control/SMA lines are to each other has not been adequately addressed and may actually be a major concern for the interpretation of single cell data.\nThe individual UMAPs now presented in Supplementary Fig. 3b/c show that there are major inconsistencies within the two CTRL and the two SMA lines. While the authors focus on the inconsistencies between the 2 SMA lines (only one of two lines shows the SMA specific clusters), I am more concerned by the major discrepancy between the two control lines. One of the two control lines (C3) has disproportionately high number of NPs (neurogenic progenitors) and MNs. This is especially pronounced for MNs: in control line 1 (C1), ~15% of sequenced cells are MNs, in contrast, ~60% of cells are MNs in C3. This turns out to be approximately 600 MNs from C1 and 4200 MNs from C3 (based on total cell number provided in Supp. Table 1) that are aggregated together for downstream analysis. Proportions of MNs from SMN lines S1 and S3 are 15% and 35% corresponding to approximately 1200 and 2300 MNs that are aggregated together. I agree with the authors that iPSC lines often show inconsistent differentiation trajectories, but these inconsistencies cannot be ignored in the interpretation of the data:\n1. The authors’ claim that MNs are significantly decreased in SMA genotype or that there is a “reduction in MN generation or survival in SMA” is not consistent across lines. If we just compare control line 1 (C1) to either SMA line 1 (S1) or S3, it is not clear that there are fewer MNs in the SMA condition as compared to control. What would the Milo analysis in Figure 1g look like if the authors compared C1 vs S1, C1 vs. S3, C3 vs. S1, and C3 vs. S3. What would it look like if they compared C1 vs. C3. Would the results be consistent with the conclusion that MNs are significantly reduced in SMN compared to control, or would they show that MNs are reduced in all lines compared to C3, which would suggest the difference in aggregated CTRL vs. SMA is simply driven by the unique differentiation trajectory of the C3 iPSC line. Immunostaining for Sox2/Tuj1, while informative, is not sufficient evidence to show that there are fewer MNs in SMA organoids compared to control.\n2. The disproportionately high number of fully differentiated MNs in C3 could also skew the pseudotime analysis towards more maturity in aggregated control cells (because the distributions of cells in C3 is uniquely skewed towards the right side of the UMAP in Figure 2f compared to C1, S1, S3). C3 also has many more cells than C1 (7308 vs. 4645) which could further contribute to technical imbalance in the pseudotime analysis. If pseudotime analysis is done on C1 vs. S1, C1 vs. S3, C3 vs. S1, C3 vs. S3, C1 vs. C3 would the results be consistent with the idea that the difference currently shown is SMA dependent?\n3. The MNs that are specifically enriched in C3 seem to have a distinct enough gene expression pattern that they make a clearly separate cluster in the aggregated UMAP (bottom cluster of MNs in Fig. 1c, Supp Figure 3b). Lines C1, S1, and S2 do not have a significant number of MNs with this signature. While I agree with the authors that comparing these subtypes to in vivo subtypes is not meaningful, this difference is transcriptional profile is still a concern because it is unclear how if affects the trajectory analysis of MNs in Figure 3. Again, would these analyses give consistent results if C1 vs. S1, C1 vs. S3, C3 vs. S1, and C3 vs. S3 comparisons were performed?\nAt the very least, the manuscript should mention the discrepancy between the two control lines, and mention that the analysis might be skewed because iPSC line C3 contains disproportionately higher numbers of differentiated MNs which also have a different transcriptional signature. Adding additional line to line (C1 vs. S1, etc) comparisons would be ideal, as it would provide a clearer picture of conclusions that are consistent across lines."}, "author_response": "Rebuttal Letter\nPOINT-BY-POINT RESPONSE TO REVIEWERS\nResponse to Reviewer 3's comment:\nWe added the statistical comparisons (*, ***, ns) between CTRL and SMA to Figures 2c and 2d as requested.\nResponse to Reviewer 4:\nWe sincerely thank the reviewer for their thorough and constructive evaluation of our revised manuscript. We are encouraged that our core findings regarding apoptosis modules, motor neuron-specific signatures, and differential gene expression analysis have been well-received.\nWe fully acknowledge the reviewer's important concerns regarding cell line variability and its potential impact on our interpretations. In response, we have conducted additional analyses that we believe support our conclusions.\nAddressing the consistency of MN reduction across lines (Question 1):\nWe thank the reviewer for raising the important point regarding the consistency of motor neuron (MN) reduction across individual line comparisons, and for suggesting pairwise testing.\nThe Milo pipeline builds its statistics on replicates, which allows to increase robustness to sample variability. Because our dataset includes replicates for each individual line, we were able to extend the analysis and run Milo differential abundance testing between all individual lines, thereby directly addressing the reviewer’s suggestion.\nIn the original analysis (which pooled all lines together), Milo testing supported a reduction of MN-related nodes in SMA compared to controls. The new pairwise analysis (performed by cell line) confirms that both SMA lines contribute to this effect, although in line-dependent directions (Figure below and Extended Data Figure 5). Specifically, one SMA line (S1) shows consistent reductions relative to both controls (C1 and C3), while the other (S3) shows a reduction compared to C3 but a relative increase compared to C1, where MN abundance is already limited. As the reviewer suggested, this heterogeneity likely reflects intrinsic variability among control lines (C1 vs. C3), rather than undermining the overall conclusion of reduced MNs in SMA. Similar variability has also been reported by other groups investigating the reproducibility of different spinal cord spheroid protocols1.\nTo minimize potential line-driven effects, these analyses could be extended to a larger set of patient-derived lines or complemented with isogenic lines in which specific mutations are corrected. While we acknowledge and reiterate the differences between our model and that of Grass et al.2, as discussed in the previous revision, it is important to note that a reduction in MN abundance was also observed in the isogenic SMA organoids they generated. Importantly, abundance-independent analyses – including pseudotime scoring, pseudobulk differential gene expression, MN trajectory inference, apoptosis module activation, and transcription factor activity (SCENIC) – also converge on a consistent MN-specific defect in SMA compared to controls.\nAddressing pseudotime and trajectory concerns (Questions 2 and 3):\nThe reviewer raised the possibility that higher MN abundance in the control C3 line could bias our developmental/pseuodtime scoring analyses. We wish to clarify that our original analysis was normalized by subsetting the same number of cells per phenotype when comparing pseudotime distributions with the KS test. This approach already ensured fair comparisons and reduced the risk of artificially low p-values, given the sensitivity of the KS test to very large sample sizes. Nevertheless, following the reviewer’s suggestion, we have now further extended the analysis by performing stratified downsampling at the line level (rather than by phenotype) and building pairwise statistics between individual lines. The results were highly consistent with previous findings: every pairwise control–SMA comparison revealed significant developmental delays in SMA, independently from the pairs considered (KS tests: C1–S1, p = 0.015; C1–S3, p = 2.32×10⁻⁹; C3–S1, p = 2.79×10⁻²⁷; C3–S3, p = 2.32×10⁻⁹). Importantly, when restricting the analysis to MNs only – thus removing any influence of differences in cell type composition – the differences in pseudotime scoring remained strongly significant (all p < 0.001) in all comparison, further supporting the aberrant MN differentiation caused by SMA.\nTrajectory analyses further confirmed this conclusion. Across all line comparisons, SMA consistently showed the same polarized pattern: an accumulation of cells in progenitor and intermediate states with impaired progression toward terminally differentiated MNs. This effect was reproducible regardless of the control line used as reference and therefore cannot be attributed to C3-specific features. All these new analyses are now implemented in the text and in the supplementary material (Extended Data Figure 9).\nFinally, we acknowledge that C3 MNs form a distinct transcriptional cluster, reflecting inter-line variability. Nevertheless, the pathological signatures of SMA – developmental delay, altered trajectories, and apoptotic modules – were reproducible in all comparisons, demonstrating that these are disease mechanisms rather than artifacts of line composition.\nAcknowledging limitations and strengthening our conclusions:\nWe have substantially revised our Discussion section to explicitly acknowledge the variability between iPSC lines. We now clearly state:\n\"While the developmental phenotype associated with SMA remained consistently detectable and statistically significant, even when analyses were performed within individual genotypes, a limitation of the SCO system is the variability in the representation of different cell populations across genotypes, and some differences in MN abundance between iPSC lines is to be acknowledged, especially in control lines. We recognize that performing analyses on hiPSC lines derived directly from patients offers the advantage of working with patient-specific material, thereby encompassing broader biological variability. As a result, significant findings are more likely to be clinically relevant. On the other hand, the use of isogenic lines would allow a more precise attribution of phenotypic differences to the disease background rather than inter-individual variation, and therefore, this adds a potential limitation to our study.\"\nIn conclusion, our additional analyses comprehensively address the reviewer's concerns while strengthening our manuscript. The convergence of evidence from multiple analytical approaches, applied across individual line comparisons, provides robust support for our central thesis that SMA involves fundamental disruptions in neural development that extend beyond simple motor neuron loss. We are grateful for the reviewer's insightful comments, which have prompted us to perform these additional validations that ultimately reinforce the biological significance of our findings.\n1. Andersen, J. et al. Generation of Functional Human 3D Cortico-Motor Assembloids. Cell 183, 1913-1929.e26 (2020).\n2. Grass, T. et al. Isogenic patient-derived organoids reveal early neurodevelopmental defects in spinal muscular atrophy initiation. Cell Rep Med 5, 101659 (2024)."}, {"round": 4, "reviewer_comments": {"reviewer_4": "The authors have reasonably addressed all my concerns regarding variability in cell lines, and made a convincing case that differences between SMA and CTRL lines in the trajectory analysis are independent of cell line variability. The included text in the discussion clarifies and strengthens the manuscript."}, "author_response": ""}]

Decision letter