Decoding neurodegeneration one cell at a time

Neurodegenerative diseases are characterized by protein misfolding and the selective vulnerability of specific neuronal subtypes. This selective vulnerability presents a paradox; most neurodegenerative disease genes are expressed broadly throughout the brain, and some ubiquitously, but only certain types of neurons are lost while others are resistant. The molecular basis for selective neuronal vulnerability has remained a mystery, but recent genomics technological innovations are starting to provide mechanistic insights. Here, we review how single-cell genomics techniques — single-cell transcriptomics, single-cell epigenomics, and spatial transcriptomics — advance our molecular understanding of selective vulnerability and neurodegeneration across Alzheimer disease, Parkinson disease, amyotrophic lateral sclerosis, frontotemporal dementia, and Huntington disease. Together, these approaches reveal the cell types affected in disease, define disease-associated molecular states, nominate candidate determinants of vulnerability and degeneration, and situate degenerating neurons within their local tissue context. Continued development and application of these techniques, including single-cell perturbation screens, will expand descriptive atlases of relevant cell types in health and disease and identify causal mechanisms, revealing the molecular basis of vulnerability and degeneration and informing therapeutic development.

AD. AD, the most common neurodegenerative disorder, typically progresses from mild cognitive impairment to severe dementia. Neuropathologically, AD is defined by amyloid plaques and tau tangles and follows a stereotyped trajectory in which entorhinal, hippocampal, and neocortical neurons are selectively lost, alongside vulnerable subcortical and interneuron populations (32, 33). Single-cell technologies now allow these patterns of selective vulnerability to be explored with molecular resolution.

Early snRNA-seq studies provided the first atlases of the AD brain, revealing cell-type-specific changes across neurons, microglia, astrocytes, oligodendrocyte lineages, and endothelial cells (34–36). As datasets grew, neuronal subtypes with distinct molecular signatures of vulnerability were identified. Leng et al. showed that RORB⁺ excitatory neurons in entorhinal cortex layer 2 are selectively vulnerable in AD, accompanied by the early downregulation of synaptic genes prior to cell death (37). Building on this, Consens et al., Cain et al., and Mathys et al. reported selective loss of SST⁺ interneurons in the frontal cortex (38–40). Gabitto et al. extended these findings by showing in the middle temporal gyrus that SST⁺ interneurons are lost early in disease, while excitatory neurons and PVALB⁺ and VIP⁺ inhibitory neuron subtypes decline later, clarifying the temporal sequence of neuronal vulnerability in this brain region (41). Rexach et al. additionally found selective depletion of a layer 5 intratelencephalic excitatory neuron population marked by expression of RORB and NEFM in AD (42). As a complement to studies with postmortem samples, Gazestani et al. analyzed surgical biopsy tissue from living individuals with varying degrees of AD pathology and uncovered a hyperactive state preceding excitatory neuron loss (43). A meta-analysis incorporating postmortem snRNA-seq datasets revealed depletion of NDNF-PROX1⁺ layer 1 interneurons and LINC00507-COL5A2⁺ layer 2/3 telencephalic neurons in samples with mild amyloid pathology (43). Together, these studies delineate the transcriptomic signatures of selectively vulnerable neuronal subtypes in AD and chart their molecular changes with disease.

Single-cell studies have made it clear that non-neuronal populations, such as microglia, are integral to AD pathogenesis. Prater et al. reported increased abundance of a microglia subcluster enriched for lysosomal/vesicular function, interferon regulatory factor, and inflammasome activation genes in disease, along with an AD-specific subcluster within homeostatic-marker-expressing microglia (44). Rexach et al. further found an AD-specific amyloid-associated microglia cluster marked by ITM2B, which may be protective (42). Gabitto et al. showed that inflammatory microglia appear early in AD in the middle temporal gyrus, preceding loss of excitatory neurons, highlighting glia as potential early drivers of disease (41).

Astrocytes also undergo prominent AD-associated changes. Serrano-Pozo et al. found that the proportion of homeostatic, intermediate, and reactive astrocytes varied across regions, while other subclusters changed with AD pathology stage (45). Consistent with this, Gabitto et al. reported early reactive astrocytes in the middle temporal gyrus that appear before excitatory neuron loss (41). Rare cases of protection from AD pathology highlight how glial response may mitigate disease. For example, Almeida et al. reported a familial AD gene mutation carrier who also harbored two copies of a protective genetic mutation (46). She remained cognitively intact into her 70s and, after her death, snRNA-seq revealed astrocytic LRP1 upregulation, consistent with enhanced tau uptake and clearance and a protective glial response (46).

Additionally, other non-neuronal cell types are affected in AD. Gabitto et al. observed early loss of oligodendrocytes in disease, accompanied by a remyelination program in oligodendrocyte precursor cells (41). Vascular cells exhibit AD-associated transcriptional changes; Yang et al. generated a molecular atlas of the human brain vasculature and identified AD-associated expression changes consistent with dysregulated blood flow (47), and Sun et al. reported disease-associated transcriptional changes in cerebrovasculature cells (48). Both studies implicated AD genome-wide association study (GWAS) variants in vasculature cell types, broadening genetic risk beyond microglia and neurons (47, 48).

snATAC-seq and multiomic studies are defining the epigenetic landscapes of brain cell types and drivers of AD-associated state changes. Early scATAC-seq studies showed that AD GWAS variants are enriched in microglia-accessible chromatin, linking genetic risk to cell-type-specific regulatory elements (49). Liu et al. extended this framework to multiregion single-nucleus epigenomic and multiomic profiling across AD pathology progression (50). They found widespread epigenomic relaxation and information loss with advancing pathology, alongside preserved epigenomic stability in cognitively resilient individuals (50). Single-cell epigenomic and multiomic studies have identified disease-relevant transcription factors in AD by linking cell-type-specific chromatin accessibility and motif enrichment to AD risk loci and disease-associated states in glial cells, implicating these factors as potential regulators of cell state transitions (41, 42, 51–54).

Spatially resolved approaches have deepened our understanding of how these states are organized in tissue. Using STARmap with protein localization and unlimited sequencing (STARmap PLUS) in a mouse model of AD, Zeng et al. found a core-shell structure; disease-associated microglia closely contact amyloid-β plaques at the core, while diseased astrocytes and oligodendrocyte precursor cells populate outer shells (55). Hyperphosphorylated tau appears predominantly in CA1 excitatory neurons and tracks with local enrichment of oligodendrocyte subtypes (55). Gabitto et al. leveraged MERFISH spatial transcriptomics to validate and refine single-cell findings in AD cortex, confirming loss of vulnerable neurons and adding spatial context that dissociated snRNA-seq alone could not provide (41).

Together, these studies identify selectively vulnerable neurons (e.g., RORB⁺ excitatory, SST⁺ interneurons) as well as disease-associated transcriptional changes in glial and vascular cells. Multiomic analyses implicate transcriptional regulators of these states, while spatial transcriptomics reveals how these cell types are organized around AD pathology (Figure 3).

Figure 3

Insights into neurodegenerative disease mechanisms from single-cell analyses. Major findings from single-cell studies for Alzheimer disease, Parkinson disease, amyotrophic lateral sclerosis, frontotemporal dementia, and Huntington disease.

PD. PD is characterized by classic motor symptoms such as bradykinesia and tremor, as well as non-motor symptoms such as depression, REM sleep behavior disorder, and orthostatic hypotension (56, 57). A neuropathological hallmark of PD is the presence of α-synuclein aggregates known as Lewy bodies and Lewy neurites, accompanied by the selective loss of dopaminergic neurons (DaNs) in the substantia nigra pars compacta (SNpc) (57, 58). The mechanisms underlying selective neuronal vulnerability in PD have been challenging to dissect, but early single-cell studies confirmed the loss of these neurons and, in doing so, began to unravel the molecular features that confer their vulnerability.

Major advancements in defining vulnerable neuronal populations in PD came from single-cell transcriptomic profiling of human postmortem tissue. Kamath et al. used single-cell genomic profiling of DaNs from patients with PD and age-matched controls, identifying a highly susceptible subpopulation marked by AGTR1 expression, predominantly localized to the ventral tier of the SNpc (59). This population exhibited strong upregulation of TP53- and NR2F2-regulated genes, implicating these molecular pathways in neurodegeneration (59). Importantly, this population was preferentially enriched for genetic PD risk factors, pointing to cell-intrinsic mechanisms as key contributors of DaN selective vulnerability (59).

Smajić et al. performed snRNA-seq on postmortem midbrain tissue from patients with idiopathic PD and controls, identifying a neuronal cluster derived almost exclusively from PD samples (60). These cells were characterized by high expression of CADPS2, a calcium-dependent activator of secretion, and shared key transcriptional features with DaNs (60). However, they exhibited reduced expression of TH (tyrosine hydroxylase) and increased expression of TIAM1, a regulator of DaN differentiation, suggesting that they may represent a transitional or degenerating state (60).

Vulnerability to α-synuclein pathology and cell death in PD extends beyond DaNs. Zeng et al. constructed an extensive transcriptional atlas by combining published snRNA-seq datasets from patients with PD and controls, revealing widespread neuronal loss affecting multiple midbrain populations, including glutamatergic, GABAergic, and dopaminergic neurons (61). This comprehensive approach identified necroptosis as a key pathway underlying neuronal degeneration, with upregulation of necroptosis-related genes observed across all three vulnerable neuronal populations (61). Complementing these findings, Martirosyan et al. analyzed large-scale transcriptome data and found that both depleted GABAergic and dopaminergic neuronal subpopulations shared molecular signatures related to protein misfolding stress, oxidative damage, metabolic dysfunction, and disrupted iron homeostasis (62). This convergence of pathological pathways across different neuronal types suggests common vulnerability mechanisms underlying PD pathogenesis.

Beyond the midbrain, single-cell genomics has revealed that cortical regions also exhibit vulnerability to PD pathology. Using spatial transcriptomics to compare whole-transcriptome signatures of cortical neurons with and without α-synuclein pathology, Goralski et al. identified specific classes of excitatory neurons — layer 5 intratelencephalic and layer 6b neurons — that are particularly susceptible to developing Lewy pathology in both patients with PD and mouse models (63). They also discovered conserved gene expression changes in aggregate-bearing neurons, which they termed the Lewy-associated molecular dysfunction from aggregates (LAMDA) signature, providing insight into the molecular consequences of protein aggregation across distinct brain regions (63).

Single-cell genomics has also contributed to shifting the understanding of PD from a neuron-centric model to a complex system involving widespread changes across non-neuronal cells, including glia and brain-resident T cells. Studies using snRNA-seq and spatial transcriptomics in human postmortem tissues have identified specific dysregulations in astrocytes and microglia, including altered cytokine signaling and the adoption of proinflammatory states, alongside a notable enrichment of T cells in affected brain regions (60, 64, 65). These findings highlight the role of spatially restricted neuroinflammatory niches formed through interactions among multiple glial and immune cell types, which may serve as focal sites of neurodegeneration (64, 65).

Collectively, single-cell approaches provide a comprehensive picture of the specific neuronal populations vulnerable in PD, ranging from DaNs in the substantia nigra to layer 5 intratelencephalic and layer 6b neurons in the cortex. These methods also reveal widespread responses and contributions from multiple brain cell types, including astrocytes, microglia, and brain-resident T cells to disease pathogenesis (Figure 3).

ALS and FTD. ALS presents with progressive muscle weakness, paralysis, and ultimately respiratory failure due to degeneration of upper motor neurons in the motor cortex and lower motor neurons in the brainstem and spinal cord (66). Pathology most often involves nuclear loss and cytoplasmic aggregation of TDP-43, with rarer familial forms showing FUS or SOD1 inclusions. Vulnerability is selective; Betz cells and fast-fatigable α-motor neurons are especially affected, whereas γ- and visceral motor neurons in the spinal cord are largely spared (67). FTD manifests with behavioral and/or language decline from frontal and temporal lobe degeneration (68). Most cases show inclusions of TDP-43, TAF15, or tau (69). The behavioral variant of FTD (bvFTD) targets the anterior cingulate and frontoinsular cortex, where von Economo neurons (VENs) and fork cells (layer 5 projection neurons) are highly vulnerable, alongside subsets of layer 2/3 excitatory neurons (70, 71). Importantly, ALS and FTD share genetic risk factors, including a hexanucleotide repeat expansion in C9ORF72, helping explain their clinical overlap in some patients and common TDP-43 pathology (66, 72).

scRNA-seq and snRNA-seq have progressively defined which cell types are most affected and how. Starting in the adult mouse spinal cord, Blum et al. and Alkaslasi et al. profiled lower motor neuron subtypes, providing reference atlases of α-, γ-, and visceral motor neurons and their transcriptional diversity (73, 74). Extending into the human spinal cord, Gautier et al. sequenced a small set of lower motor neurons, including α and γ subtypes (75). This work demonstrates feasibility but also underscores the rarity of lower motor neurons as a challenge to robustly characterizing their heterogeneity in health and disease.

Turning to human cortex, Li et al. used snRNA-seq to show that in C9ORF72 expansion carriers with ALS, there is notable transcriptional dysregulation in layer 2/3 and 5/6 excitatory neurons, including an increase in proteostasis genes and a decrease in neuronal function genes, whereas fewer high-quality neurons were recovered in C9-FTD (76). Gittings et al. applied snRNA-seq to frontal and occipital cortices from C9ORF72 patients spanning the ALS/FTD disease spectrum and detected cryptic exons in TDP-43 target genes with single-cell resolution (77). STMN2 and KALRN cryptic exon-containing transcripts were enriched in excitatory neurons, particularly in C9-FTD, with the highest proportion in layer 5 extratelencephalic neurons — a population that is transcriptionally similar to VENs and fork cells (77). Scaling further, Pineda et al. used snRNA-seq to profile ALS motor cortex and FTD prefrontal cortex and found that Betz cells and VENs are nearly indistinguishable transcriptionally and belong to a broader population of VAT1L⁺ layer 5 neurons that are enriched for ALS/FTD-linked genes and exhibit dysregulation of primary cilia and axoneme-related genes in disease (78). Additional work suggests that this VAT1L⁺ population (also marked by GABRQ) is depleted in ALS and FTD (78, 79). Pineda and colleagues also identified a layer 3/5 SCN4B⁺ excitatory neuron population in the motor cortex, which may be disease relevant and warrants further investigation (78). Limone et al. applied snRNA-seq to ALS and control cortex samples and found that THY1⁺ extratelencephalic neurons have higher expression of ALS/FTD risk genes than other cortical neurons, even in controls, potentially contributing to their inherent susceptibility in disease (80). In ALS, extratelencephalic neurons exhibited an induction of genes involved in protein homeostasis and stress responses (80). Furthermore, in bvFTD, Rexach et al. found selective vulnerability of layer 2/3 intratelencephalic neurons as well as enrichment of a layer 2/3 excitatory neuron cluster in the insular cortex marked by increased expression of multiple bvFTD/ALS risk genes (42).

Besides neurons, across ALS/FTD, astrocytes, oligodendrocyte lineage cells, and microglia show prominent disease-associated shifts, including astrocyte activation and cytoskeletal changes, downregulation of oligodendrocyte genes involved in myelination, differentiation, and axonal support, and loss of homeostatic programs accompanied by induction of reactive, interferon-responsive, and endolysosomal stress pathways (76, 78, 80, 81). Endothelial cells downregulate tight and adherens junction genes consistent with blood-brain-barrier dysfunction, alongside disease-associated changes in immune cell composition, including depletion of potentially protective microglial subtypes and enrichment of nonmicroglial immune cells like natural killer and T cells in affected regions (78, 81).

Li et al. generated a cortical snATAC-seq resource, defining cell-type-specific chromatin landscapes in control, ALS, and FTD conditions (76). Even though differentially accessible regions between the control and disease conditions were limited, comparison with their snRNA-seq data revealed concordant gene-activity and expression changes in glia, consistent with epigenetic remodeling in disease (76). Rexach et al. used snATAC-seq to identify transcription factor drivers of disease-associated cell states across dementias (42). In bvFTD, transcription factor footprinting showed increased RORB binding in vulnerable layer 2/3 excitatory neurons from insula, complementing the upregulation of RORB expression in these neurons in their snRNA-seq data (42). Most RORB target genes were downregulated in disease, suggesting a potential dampening of neuroprotective pathways (42). Rexach and colleagues further found an increase in chromatin accessibility at RUNX1 binding sites as well as upregulation of IKZF1 regulon target genes in bvFTD microglia, implicating these transcription factors in reactive microglial states (42).

A coherent picture emerges: long-range projection neurons, including Betz cells in ALS and VENs in FTD, are especially vulnerable, showing shared disease-associated transcriptional signatures and enrichment of ALS/FTD risk genes. Lower motor neurons remain central but difficult to interrogate because of their rarity. Glial populations exhibit loss of homeostatic functions and adoption of disease-associated or reactive states that may contribute to disease (Figure 3).

HD. HD is a monogenic neurodegenerative disorder caused by a CAG-repeat expansion in exon 1 of the huntingtin (HTT) gene (82, 83). Although HTT is ubiquitously expressed, the disease is characterized by preferential degeneration of medium spiny neurons (MSNs) in the striatum and deep-layer pyramidal neurons in the cortex, which are critical for motor control and cognitive function (84, 85). Burns et al. integrated spatial transcriptomics with snRNA-seq in the rapidly progressing R6/2 HD mouse model and found that the most vulnerable populations — MSNs in the striatum and excitatory neurons in the cortex — exhibit marked gene dysregulation as early as postnatal day 0 (86). These changes include early metabolic and maturation defects in the caudate putamen and dysregulation of kinesin-mediated migratory pathways in the cortex (86). However, because the R6/2 model expresses only mutant HTT (mHTT) exon 1, it lacks many of the MSN-selective pathologies found in mouse models expressing full-length mHTT (87, 88). More recent work using the full-length knockin mouse model has demonstrated robust, MSN-selective transcriptomic dysregulation and nuclear HTT aggregation that more faithfully recapitulates human pathology (88).

Single-cell analyses have revealed that within the striatum, the primary affected region, neuronal vulnerability is not uniform (86). Matsushima et al. showed that among striatal projection neurons (SPNs), striosomal SPNs are more vulnerable than matrix SPNs, and within the striosomes, D2 neurons are more affected than D1 neurons (89). As the disease progresses, these distinct striatal cell types begin to lose their unique molecular identities, blurring the distinction between striosomes and the matrix (89). Complementing these observations, Burns et al. used spatial and single-nucleus analyses of the R6/2 mice to show that changes in MSN identity genes are present at birth and intensify over time, whereas other cell types lose identity later (86). Thus, HD pathogenesis involves not only cell death but also progressive, regionally patterned collapse of cellular identity (86).

An important insight from single-cell genomics is the revelation that the inherited CAG-repeat expansion seems to function as a “slowly ticking DNA clock” rather than as an immediately toxic mutation, which may help explain why HD symptoms take decades to manifest. Handsaker et al. developed an innovative approach to simultaneously measure CAG-repeat length and genome-wide RNA expression at single-cell resolution (90). Applying this approach to postmortem samples from 50 patients with HD and 53 controls, they found that the CAG repeat tract expands gradually over decades in SPNs, growing at a rate of less than one repeat per year during the first two decades of life (90). Once a cell’s repeat tract reaches a critical “tipping point” of approximately 80 CAGs, the expansion rate accelerates dramatically, and the tract can reach 150 CAGs within just a few years (90). Once this threshold of approximately 150 repeats is breached, the cell’s transcriptome becomes profoundly dysregulated, ultimately leading to rapid cell death (90).

Using the full-length mHTT mouse model, Wang et al. independently identified a similar pathological threshold of approximately 150 CAG repeats (88). Importantly, they demonstrated that mismatch repair (MMR) genes Msh3 and Pms1 drive rapid, MSN-selective CAG-repeat expansion, leading to transcriptional dysregulation and mHTT aggregation (88). Knockout of Msh3 in HD mice reduced mHtt repeat expansion in MSNs, ameliorated transcriptional dysregulation, and corrected several behavioral deficits (88). Thus, modulating MSH3/PMS1 and their encoded MMR complexes may be a promising avenue for mitigating HD pathology and potentially other repeat expansion disorders.

Using snRNA-seq in brains from patients with HD, Handsaker et al. found that SPNs exhibit the most pronounced somatic expansion of the HD-causing allele compared with other cell types, which may explain their unique vulnerability (90). Using fluorescence-activated nuclear sorting of human striatal cell types, Mätlik et al. revealed that CAG expansion is not exclusive to MSNs but also occurs in cholinergic interneurons and cerebellar Purkinje neurons (91). It seems that CAG-repeat expansion is necessary but perhaps not sufficient to drive cell death, and that thresholds for toxicity and the rate of expansion likely vary by cell type.

Beyond neurons, single-cell and spatial genomics consistently reveal profound, cell-type-specific changes in non-neuronal cells that both respond to and amplify neuronal injury (92–97). These studies converge on a unified model of HD as a disorder of progressive cellular identity collapse driven by cell-type-specific somatic instability of the HTT locus and its downstream molecular consequences. The earliest pathology arises in MSNs of the striatum, where gradual CAG expansion and transcriptional dysregulation erode cellular homeostasis, triggering loss of neuronal identity. Non-neuronal populations including astrocytes, microglia, and oligodendrocytes also undergo state transitions that amplify local inflammation, metabolic stress, and circuit dysfunction (Figure 3).

Single-cell studies across neurodegenerative disorders reveal shared and disease-specific features in neuronal and glial responses. Vulnerable neuronal populations frequently include large, metabolically demanding projection neurons like RORB-expressing corticocortical neurons in AD (37), SNpc dopaminergic neurons in PD (59), Betz cells and spinal α-motor neurons in ALS (67, 78), von Economo neurons in FTD (78), and medium spiny neurons in HD (86, 90). Although these neuronal classes differ across disorders, many exhibit overlapping disease-related transcriptional signatures involving synaptic dysfunction, mitochondrial impairment, proteostasis disruption, and induction of cellular stress-response programs (37, 59–62, 76, 80, 86, 89, 90, 98). However, aspects of neuronal responses are still disease specific. Cortical neurons show largely distinct transcriptional changes in AD versus PD (64). Genetic influences on vulnerability also differ across diseases. Genes linked to PD risk variants show enriched expression in susceptible dopaminergic neuron subtypes (59), and ALS/FTD risk genes show higher expression in vulnerable corticofugal projection neurons (78, 80), although risk genes in both disorders are also expressed in non-neuronal cell types. In contrast, AD risk loci show predominant enrichment in microglia rather than neurons, with additional contributions from cerebrovascular cells (47–49, 51). In HD, selective vulnerability appears to reflect cell-type-specific somatic expansion of the mutant HTT allele (90).

Glial and immune cells likewise display both common and disease-specific features. Microglia adopt reactive, disease-associated states involving immune and phagocytic activation in AD, PD, ALS, and HD (41, 60, 81, 98). Astrocytes likewise transition to reactive states across disorders, characterized by loss of homeostatic gene expression and activation of stress-related pathways (45, 64, 76, 92, 94). Although glial cells in AD and PD share more differentially expressed genes than neurons, supporting the presence of recurring reactive transcriptional states, disease-specific transcriptional signatures are also apparent within glial populations (64), indicating that glial responses retain both shared and distinct features across disorders.

Single-cell techniques have transformed neurodegeneration research by defining affected cell types, characterizing disease-associated states, and revealing the regulatory and spatial factors that shape these states. But key challenges remain. Isolation of whole cells from CNS tissue for scRNA-seq can distort cellular representation by enriching or depleting particular populations and can induce dissociation-associated transcriptional responses (99–102). These artifacts are substantially reduced with nuclei isolation, which avoids enzymatic digestion, enables the use of frozen postmortem CNS tissue, and better preserves the native diversity of cell types (12, 99, 103). However, snRNA-seq captures only nuclear RNA and therefore misses transcripts localized to the soma and neuronal processes, leaving key aspects of neuronal biology underrepresented. Both scRNA-seq and snRNA-seq also exhibit notable dropout events (i.e., failure to detect expressed transcripts owing to limited mRNA capture efficiency), which disproportionately affects low-abundance genes and contributes to sparsity in single-cell datasets (104, 105). Spatial transcriptomics helps address some of these limitations by profiling RNA within intact tissue, though current platforms remain expensive, low-throughput, and difficult to scale to large cohorts, and depending on the platform, they may provide lower transcriptomic coverage than dissociative approaches (105).

Spatial transcriptomics is rapidly advancing and newer high-resolution assays now achieve single-cell or even subcellular spatial readouts with improved detection efficiency, and next-generation platforms increasingly support whole-transcriptome and even isoform-resolved measurements (106). This is an important development for diseases such as ALS/FTD, where RNA splicing defects (107–111) and alternative polyadenylation changes (112–114) are central to disease pathogenesis. Several spatial platforms support joint RNA-protein detection, enabling direct comparisons between neighboring cells with and without disease-associated pathology (e.g., TDP-43 aggregates), clarifying the molecular consequences of pathological inclusions.

A major challenge for spatial and single-nucleus profiling of postmortem human CNS tissue is obtaining enough high-quality samples and profiling sufficient numbers of cells within them. Many neuronal populations of interest, such as spinal motor neurons, are intrinsically rare (75), and neurodegenerative disease further depletes the most vulnerable types, limiting their representation in postmortem datasets. Sample quality also varies widely in postmortem human CNS tissue, with factors such as RNA integrity, postmortem interval, and agonal state contributing to variability in transcriptomic measurements (115–117). Because these factors are often outside experimental control, they remain important determinants of dataset quality and key factors complicating efforts to build comprehensive and comparable human single-nucleus and spatial transcriptomic resources.

Limited temporal resolution represents another major challenge. Neurodegenerative diseases unfold over years to decades, yet most human single-cell studies sample only late pathological stages, making it difficult to distinguish early causal events from downstream or compensatory responses. Integrating datasets collected across pathological stages can, in principle, help bridge this temporal gap, but early and intermediate-stage samples remain uncommon because these studies rely largely on postmortem tissue. When staging information is available, such as the pathology-based staging in Gazestani et al. and Gabitto et al. (41, 43), it provides a framework for inferring early or intermediate states that are difficult to discern when analyzing only the most severely affected cases. Computational approaches like pseudotime analysis can provide an inferred ordering of cells along putative disease progression, offering hypotheses about molecular trajectories and potential early events (118, 119).

Model systems provide complementary opportunities to capture temporal dynamics experimentally. Genetically engineered mouse models allow in vivo analysis across defined disease stages and can reveal circuit-level or cell-cell interactions that are impossible to assess in vitro. However, even the best models only approximate human disease and vary widely in how well they recapitulate human selective vulnerability, molecular pathology, and disease pathogenesis. Human induced pluripotent stem cell– (iPSC-), organoid-, and assembloid-based systems enable the study of human-specific cell types and permit controlled perturbations, but lack the full cellular, circuit, and immune complexity of the CNS in vivo (120–122). Together, these systems can be used to provide essential mechanistic insight, yet they complement, rather than replace, postmortem human studies.

The growing use of “multiomics” in neurodegeneration research promises to link regulatory landscapes with transcriptional outputs at single-cell resolution. However, RNA and chromatin accessibility are sometimes generated in parallel rather than from the same nucleus, which can make it difficult to assign specific regulatory elements to downstream gene expression with confidence. Joint multiome assays help mitigate this limitation, although they introduce their own technical biases. As datasets generated across platforms, donors, and laboratories continue to expand, batch effects, reproducibility, standardized preprocessing, and robust cross-cohort harmonization will become increasingly important. Meta-analytic efforts further highlight the value of robust data integration, as cross-cohort analyses can reveal cellular insights that individual studies may not detect on their own.

Emerging single-cell omics technologies are expanding profiling beyond transcriptomics and chromatin accessibility. These include single-cell genome sequencing to detect somatic mutations and repeat expansions (90, 123, 124), DNA methylation to assess epigenetic remodeling (125), CUT&Tag to profile histone marks and transcription factor binding (126), chromatin conformation assays to map long-range interactions (127, 128), and proteomics to bridge discrepancies between RNA abundance and protein levels (129). Single-cell proteomics has been applied to spinal motor neurons in ALS, establishing a foundation for pathology-guided proteomic profiling at single-cell resolution (130). Extending this approach across neurodegenerative disease contexts and regions of the CNS could reveal how distinct pathological proteins reshape cellular proteomes and identify potential therapeutic targets that are only evident at the protein level. For repeat-expansion diseases, proof-of-principle work in HD has linked CAG-repeat length to transcriptomes at single-cell resolution (90); analogous approaches for other neurodegenerative disease genes, such as C9ORF72 hexanucleotide expansions in ALS/FTD, would directly connect repeat length with transcriptional consequences, unifying genotype and molecular phenotype within individual cells.

Because molecular changes in disease may reflect either pathogenic or compensatory processes, functional studies are essential to pinpoint drivers of neurodegeneration. A practical path from observation to mechanism involves nominating candidate genes from descriptive datasets, perturbing them in relevant model systems, and evaluating whether the resulting cell states recapitulate or modify those seen in disease. Perturbation-based approaches (e.g., Perturb-seq and pooled overexpression screens with sc/snRNA-seq readouts) directly test whether specific genes drive or reverse disease-associated states, moving the field from description toward causality (131) (Figure 4). In one in vitro single-cell perturbation screen, Dräger et al. performed a CRISPRi CROP-seq screen in iPSC-derived microglia, showing that these cells adopt a spectrum of states resembling those observed in human brains and identifying regulators of these states (132). For example, they found that CSF1R knockdown reduced the proportion of cells in an SPP1⁺, disease-associated state (132). Recent in vivo Perturb-seq studies demonstrate that pooled CRISPR screens with single-cell readouts can be applied to the mammalian brain (133–135). Extending these strategies to neurodegenerative disease models will enable direct testing of causal mechanisms underlying neuronal selective vulnerability and degeneration to identify candidate therapeutic strategies.

Figure 4

Single-cell perturbation framework to test causal regulators of vulnerability and degeneration. Left: Disease models — in vitro (e.g., iPSC-derived neurons or glia) and in vivo (e.g., gene-edited or transgenic mice) models that are relevant for a neurodegenerative disease. Middle: Perturbation — reduced expression (e.g., knockout) or increased expression (e.g., overexpression) of candidate genes or the whole genome in a pooled or arrayed format. Right: Single-cell readout — scRNA-seq, scATAC-seq, multiome, or spatial assays to quantify shifts in cell-state composition, transcription factor motif accessibility/expression, and neuronal survival due to a given perturbation. This framework links genetic/perturbational inputs to cell-state outcomes, enabling causal inference and therapeutic target selection.

This work is the result of NIH funding, in whole or in part, and is subject to the NIH Public Access Policy. Through acceptance of this federal funding, the NIH has been given a right to make the work publicly available in PubMed Central.

• NIH grants R35NS137159, U54NS123743, and R01AG064690.
• Live Like Lou Graduate Fellowship (to TPN).
• Target ALS (to ADG).
• SMA Foundation (to ADG).
• Stanford Graduate Fellowship (to TPN).
• Biohub – San Francisco Investigator grant (to ADG).

Conflict of interest: ADG is a scientific founder of Maze Therapeutics, Trace Neuroscience, and Lyterian Therapeutics.

Copyright: © 2026, Gautier et al. This is an open access article published under the terms of the Creative Commons Attribution 4.0 International License.

Reference information: J Clin Invest. 2026;136(6):e199841. https://doi.org/10.1172/JCI199841.

1. Wilson DM, et al. Hallmarks of neurodegenerative diseases. Cell. 2023;186(4):693–714.
   View this article via: CrossRef PubMed Google Scholar
2. Dugger BN, Dickson DW. Pathology of neurodegenerative diseases. Cold Spring Harb Perspect Biol. 2017;9(7):a028035.
   View this article via: CrossRef PubMed Google Scholar
3. Kampmann M. Molecular and cellular mechanisms of selective vulnerability in neurodegenerative diseases. Nat Rev Neurosci. 2024;25(5):351–371.
   View this article via: CrossRef PubMed Google Scholar
4. Fu H, et al. Selective vulnerability in neurodegenerative diseases. Nat Neurosci. 2018;21(10):1350–1358.
   View this article via: CrossRef PubMed Google Scholar
5. Saxena S, Caroni P. Selective neuronal vulnerability in neurodegenerative diseases: from stressor thresholds to degeneration. Neuron. 2011;71(1):35–48.
   View this article via: CrossRef PubMed Google Scholar
6. Tang F, et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6(5):377–382.
   View this article via: CrossRef PubMed Google Scholar
7. Macosko EZ, et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 2015;161(5):1202–1214.
   View this article via: CrossRef PubMed Google Scholar
8. Klein AM, et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell. 2015;161(5):1187–1201.
   View this article via: CrossRef PubMed Google Scholar
9. Kolodziejczyk AA, et al. The technology and biology of single-cell RNA sequencing. Mol Cell. 2015;58(4):610–620.
   View this article via: CrossRef PubMed Google Scholar
10. Stuart T, Satija R. Integrative single-cell analysis. Nat Rev Genet. 2019;20(5):257–272.
    View this article via: CrossRef PubMed Google Scholar
11. Grindberg RV, et al. RNA-sequencing from single nuclei. Proc Natl Acad Sci U S A. 2013;110(49):19802–19807.
    View this article via: CrossRef PubMed Google Scholar
12. Habib N, et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods. 2017;14(10):955–958.
    View this article via: CrossRef PubMed Google Scholar
13. Buenrostro JD, et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature. 2015;523(7561):486–490.
    View this article via: CrossRef PubMed Google Scholar
14. Cusanovich DA, et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science. 2015;348(6237):910–914.
    View this article via: CrossRef PubMed Google Scholar
15. Ståhl PL, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353(6294):78–82.
    View this article via: CrossRef PubMed Google Scholar
16. Rodriques SG, et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019;363(6434):1463–1467.
    View this article via: CrossRef PubMed Google Scholar
17. Chen KH, et al. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015;348(6233):aaa6090.
    View this article via: CrossRef PubMed Google Scholar
18. Wang X, et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science. 2018;361(6400):eaat5691.
    View this article via: CrossRef PubMed Google Scholar
19. Chen A, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022;185(10):1777–1792.
    View this article via: CrossRef Google Scholar
20. Cheng Y, et al. Sequencing-free whole-genome spatial transcriptomics at single-molecule resolution. Cell. 2025;188(24):6953–6970.
    View this article via: CrossRef PubMed Google Scholar
21. Zhao Y, et al. Stereo-seq V2: Spatial mapping of total RNA on FFPE sections with high resolution. Cell. 2025;188(23):6554–6571.
    View this article via: CrossRef PubMed Google Scholar
22. Adamson B, et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell. 2016;167(7):1867–1882.
    View this article via: CrossRef PubMed Google Scholar
23. Dixit A, et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell. 2016;167(7):1853–1866.
    View this article via: CrossRef PubMed Google Scholar
24. Jaitin DA, et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-Seq. Cell. 2016;167(7):1883–1896.
    View this article via: CrossRef PubMed Google Scholar
25. Replogle JM, et al. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. Cell. 2022;185(14):2559–2575.
    View this article via: CrossRef PubMed Google Scholar
26. Mimitou EP, et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat Methods. 2019;16(5):409–412.
    View this article via: CrossRef PubMed Google Scholar
27. Datlinger P, et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat Methods. 2017;14(3):297–301.
    View this article via: CrossRef PubMed Google Scholar
28. Liscovitch-Brauer N, et al. Profiling the genetic determinants of chromatin accessibility with scalable single-cell CRISPR screens. Nat Biotechnol. 2021;39(10):1270–1277.
    View this article via: CrossRef PubMed Google Scholar
29. Rubin AJ, et al. Coupled single-cell CRISPR screening and epigenomic profiling reveals causal gene regulatory networks. Cell. 2019;176(1-2):361–376.
    View this article via: CrossRef PubMed Google Scholar
30. Pierce SE, et al. High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer. Nat Commun. 2021;12(1):2969.
    View this article via: CrossRef PubMed Google Scholar
31. Saunders RA, et al. Perturb-Multimodal: a platform for pooled genetic screens with imaging and sequencing in intact mammalian tissue. Cell. 2025;188(17):4790–4809.
    View this article via: CrossRef Google Scholar
32. Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science. 2002;297(5580):353–356.
    View this article via: CrossRef PubMed Google Scholar
33. Jucker M, Walker LC. Self-propagation of pathogenic protein aggregates in neurodegenerative diseases. Nature. 2013;501(7465):45–51.
    View this article via: CrossRef PubMed Google Scholar
34. Mathys H, et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature. 2019;570(7761):332–337.
    View this article via: CrossRef PubMed Google Scholar
35. Grubman A, et al. A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation. Nat Neurosci. 2019;22(12):2087–2097.
    View this article via: CrossRef PubMed Google Scholar
36. Lau S-F, et al. Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer’s disease. Proc Natl Acad Sci U S A. 2020;117(41):25800–25809.
    View this article via: CrossRef PubMed Google Scholar
37. Leng K, et al. Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease. Nat Neurosci. 2021;24(2):276–287.
    View this article via: CrossRef PubMed Google Scholar
38. Consens ME, et al. Bulk and single-nucleus transcriptomics highlight intra-telencephalic and somatostatin neurons in Alzheimer’s disease. Front Mol Neurosci. 2022;15:903175.
    View this article via: CrossRef PubMed Google Scholar
39. Cain A, et al. Multicellular communities are perturbed in the aging human brain and Alzheimer’s disease. Nat Neurosci. 2023;26(7):1267–1280.
    View this article via: CrossRef PubMed Google Scholar
40. Mathys H, et al. Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer’s disease pathology. Cell. 2023;186(20):4365–4385.
    View this article via: CrossRef PubMed Google Scholar
41. Gabitto MI, et al. Integrated multimodal cell atlas of Alzheimer’s disease. Nat Neurosci. 2024;27(12):2366–2383.
    View this article via: CrossRef PubMed Google Scholar
42. Rexach JE, et al. Cross-disorder and disease-specific pathways in dementia revealed by single-cell genomics. Cell. 2024;187(20):5753–5774.
    View this article via: CrossRef PubMed Google Scholar
43. Gazestani V, et al. Early Alzheimer’s disease pathology in human cortex involves transient cell states. Cell. 2023;186(20):4438–4453.e23.
    View this article via: CrossRef PubMed Google Scholar
44. Prater KE, et al. Human microglia show unique transcriptional changes in Alzheimer’s disease. Nat Aging. 2023;3(7):894–907.
    View this article via: CrossRef PubMed Google Scholar
45. Serrano-Pozo A, et al. Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer’s disease. Nat Neurosci. 2024;27(12):2384–2400.
    View this article via: CrossRef PubMed Google Scholar
46. Almeida MC, et al. Single-nucleus RNA sequencing demonstrates an autosomal dominant Alzheimer’s disease profile and possible mechanisms of disease protection. Neuron. 2024;112(11):1778–1794.
    View this article via: CrossRef PubMed Google Scholar
47. Yang AC, et al. A human brain vascular atlas reveals diverse mediators of Alzheimer’s risk. Nature. 2022;603(7903):885–892.
    View this article via: CrossRef PubMed Google Scholar
48. Sun N, et al. Single-nucleus multiregion transcriptomic analysis of brain vasculature in Alzheimer’s disease. Nat Neurosci. 2023;26(6):970–982.
    View this article via: CrossRef PubMed Google Scholar
49. Corces MR, et al. Single-cell epigenomic analyses implicate candidate causal variants at inherited risk loci for Alzheimer’s and Parkinson’s diseases. Nat Genet. 2020;52(11):1158–1168.
    View this article via: CrossRef PubMed Google Scholar
50. Liu Z, et al. Single-cell multiregion epigenomic rewiring in Alzheimer’s disease progression and cognitive resilience. Cell. 2025;188(18):4980–5002.
    View this article via: CrossRef PubMed Google Scholar
51. Morabito S, et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat Genet. 2021;53(8):1143–1155.
    View this article via: CrossRef PubMed Google Scholar
52. Anderson AG, et al. Single nucleus multiomics identifies ZEB1 and MAFB as candidate regulators of Alzheimer’s disease-specific cis-regulatory elements. Cell Genom. 2023;3(3):100263.
    View this article via: CrossRef PubMed Google Scholar
53. Sun N, et al. Human microglial state dynamics in Alzheimer’s disease progression. Cell. 2023;186(20):4386–4403.
    View this article via: CrossRef PubMed Google Scholar
54. Xiong X, et al. Epigenomic dissection of Alzheimer’s disease pinpoints causal variants and reveals epigenome erosion. Cell. 2023;186(20):4422–4437.
    View this article via: CrossRef PubMed Google Scholar
55. Zeng H, et al. Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in a mouse model of Alzheimer’s disease. Nat Neurosci. 2023;26(3):430–446.
    View this article via: CrossRef PubMed Google Scholar
56. Kobylecki C. Update on the diagnosis and management of Parkinson’s disease. Clin Med (Lond). 2020;20(4):393–398.
    View this article via: CrossRef PubMed Google Scholar
57. Bloem BR, et al. Parkinson’s disease. Lancet. 2021;397(10291):2284–2303.
    View this article via: CrossRef PubMed Google Scholar
58. Soto C, et al. Toward a biological definition of neuronal and glial synucleinopathies. Nat Med. 2025;31(2):396–408.
    View this article via: CrossRef PubMed Google Scholar
59. Kamath T, et al. Single-cell genomic profiling of human dopamine neurons identifies a population that selectively degenerates in Parkinson’s disease. Nat Neurosci. 2022;25(5):588–595.
    View this article via: CrossRef PubMed Google Scholar
60. Smajić S, et al. Single-cell sequencing of human midbrain reveals glial activation and a Parkinson-specific neuronal state. Brain. 2022;145(3):964–978.
    View this article via: CrossRef PubMed Google Scholar
61. Zeng X, et al. Single-cell analyses reveal necroptosis’s potential role in neuron degeneration and show enhanced neuron-immune cell interaction in Parkinson’s disease progression. Parkinsons Dis. 2023;2023:5057778.
    View this article via: PubMed CrossRef Google Scholar
62. Martirosyan A, et al. Unravelling cell type-specific responses to Parkinson’s disease at single cell resolution. Mol Neurodegener. 2024;19(1):7.
    View this article via: CrossRef PubMed Google Scholar
63. Goralski TM, et al. Spatial transcriptomics reveals molecular dysfunction associated with cortical Lewy pathology. Nat Commun. 2024;15(1):2642.
    View this article via: CrossRef PubMed Google Scholar
64. Zhu B, et al. Single-cell transcriptomic and proteomic analysis of Parkinson’s disease brains. Sci Transl Med. 2024;16(771):eabo1997.
    View this article via: CrossRef PubMed Google Scholar
65. Ma M, et al. The spatial landscape of glial pathology and T cell response in Parkinson’s disease substantia nigra. Nat Commun. 2025;16(1):7146.
    View this article via: CrossRef PubMed Google Scholar
66. Taylor JP, et al. Decoding ALS: from genes to mechanism. Nature. 2016;539(7628):197–206.
    View this article via: CrossRef PubMed Google Scholar
67. Nijssen J, et al. Motor neuron vulnerability and resistance in amyotrophic lateral sclerosis. Acta Neuropathol. 2017;133(6):863–885.
    View this article via: CrossRef PubMed Google Scholar
68. Bang J, et al. Frontotemporal dementia. Lancet. 2015;386(10004):1672–1682.
    View this article via: CrossRef PubMed Google Scholar
69. Mackenzie IRA, et al. A harmonized classification system for FTLD-TDP pathology. Acta Neuropathol. 2011;122(1):111–113.
    View this article via: CrossRef PubMed Google Scholar
70. Seeley WW, et al. Neurodegenerative diseases target large-scale human brain networks. Neuron. 2009;62(1):42–52.
    View this article via: CrossRef PubMed Google Scholar
71. Seeley WW. Selective functional, regional, and neuronal vulnerability in frontotemporal dementia. Curr Opin Neurol. 2008;21(6):701–707.
    View this article via: CrossRef PubMed Google Scholar
72. Orr HT. FTD and ALS: genetic ties that bind. Neuron. 2011;72(2):189–190.
    View this article via: CrossRef PubMed Google Scholar
73. Blum JA, et al. Single-cell transcriptomic analysis of the adult mouse spinal cord reveals molecular diversity of autonomic and skeletal motor neurons. Nat Neurosci. 2021;24(4):572–583.
    View this article via: CrossRef PubMed Google Scholar
74. Alkaslasi MR, et al. Single nucleus RNA-sequencing defines unexpected diversity of cholinergic neuron types in the adult mouse spinal cord. Nat Commun. 2021;12(1):2471.
    View this article via: CrossRef PubMed Google Scholar
75. Gautier O, et al. Challenges of profiling motor neuron transcriptomes from human spinal cord. Neuron. 2023;111(23):3739–3741.
    View this article via: CrossRef PubMed Google Scholar
76. Li J, et al. Divergent single cell transcriptome and epigenome alterations in ALS and FTD patients with C9orf72 mutation. Nat Commun. 2023;14(1):5714.
    View this article via: CrossRef PubMed Google Scholar
77. Gittings LM, et al. Cryptic exon detection and transcriptomic changes revealed in single-nuclei RNA sequencing of C9ORF72 patients spanning the ALS-FTD spectrum. Acta Neuropathol. 2023;146(3):433–450.
    View this article via: CrossRef PubMed Google Scholar
78. Pineda SS, et al. Single-cell dissection of the human motor and prefrontal cortices in ALS and FTLD. Cell. 2024;187(8):1971–1989.
    View this article via: CrossRef PubMed Google Scholar
79. Gami-Patel P, et al. The severity of behavioural symptoms in FTD is linked to the loss of GABRQ-expressing VENs and pyramidal neurons. Neuropathol Appl Neurobiol. 2022;48(4):e12798.
    View this article via: CrossRef PubMed Google Scholar
80. Limone F, et al. Single-nucleus sequencing reveals enriched expression of genetic risk factors in extratelencephalic neurons sensitive to degeneration in ALS. Nat Aging. 2024;4(7):984–997.
    View this article via: CrossRef PubMed Google Scholar
81. Tuddenham JF, et al. Single-cell transcriptomic landscape of the neuroimmune compartment in amyotrophic lateral sclerosis brain and spinal cord. Acta Neuropathol. 2025;150(1):10.
    View this article via: CrossRef PubMed Google Scholar
82. Tabrizi SJ, et al. Huntington disease: new insights into molecular pathogenesis and therapeutic opportunities. Nat Rev Neurol. 2020;16(10):529–546.
    View this article via: CrossRef PubMed Google Scholar
83. The Huntington’s Disease Collaborative Research Group. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell. 1993;72(6):971–983.
    View this article via: CrossRef PubMed Google Scholar
84. Pressl C, et al. Selective vulnerability of layer 5a corticostriatal neurons in Huntington’s disease. Neuron. 2024;112(6):924–941.
    View this article via: CrossRef PubMed Google Scholar
85. Vonsattel JP, DiFiglia M. Huntington disease. J Neuropathol Exp Neurol. 1998;57(5):369–384.
    View this article via: CrossRef PubMed Google Scholar
86. Burns MS, et al. Distinct molecular patterns in R6/2 HD mouse brain: insights from spatiotemporal transcriptomics. Neuron. 2025;113(15):2416–2437.
    View this article via: CrossRef PubMed Google Scholar
87. Langfelder P, et al. Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice. Nat Neurosci. 2016;19(4):623–633.
    View this article via: CrossRef PubMed Google Scholar
88. Wang N, et al. Distinct mismatch-repair complex genes set neuronal CAG-repeat expansion rate to drive selective pathogenesis in HD mice. Cell. 2025;188(6):1524–1544.
    View this article via: CrossRef PubMed Google Scholar
89. Matsushima A, et al. Transcriptional vulnerabilities of striatal neurons in human and rodent models of Huntington’s disease. Nat Commun. 2023;14(1):282.
    View this article via: CrossRef PubMed Google Scholar
90. Handsaker RE, et al. Long somatic DNA-repeat expansion drives neurodegeneration in Huntington’s disease. Cell. 2025;188(3):623–639.
    View this article via: CrossRef PubMed Google Scholar
91. Mätlik K, et al. Cell-type-specific CAG repeat expansions and toxicity of mutant Huntingtin in human striatum and cerebellum. Nat Genet. 2024;56(3):383–394.
    View this article via: CrossRef PubMed Google Scholar
92. Al-Dalahmah O, et al. Single-nucleus RNA-seq identifies Huntington disease astrocyte states. Acta Neuropathol Commun. 2020;8(1):19.
    View this article via: CrossRef PubMed Google Scholar
93. Reyes-Ortiz AM, et al. Single-nuclei transcriptome analysis of Huntington disease iPSC and mouse astrocytes implicates maturation and functional deficits. iScience. 2023;26(1):105732.
    View this article via: CrossRef PubMed Google Scholar
94. Paryani F, et al. Multi-omic analysis of Huntington’s disease reveals a compensatory astrocyte state. Nat Commun. 2024;15(1):6742.
    View this article via: CrossRef PubMed Google Scholar
95. Wilton DK, et al. Microglia and complement mediate early corticostriatal synapse loss and cognitive dysfunction in Huntington’s disease. Nat Med. 2023;29(11):2866–2884.
    View this article via: CrossRef PubMed Google Scholar
96. Bøstrand SMK, et al. Mapping the glial transcriptome in Huntington’s disease using snRNAseq: selective disruption of glial signatures across brain regions. Acta Neuropathol Commun. 2024;12(1):165.
    View this article via: CrossRef PubMed Google Scholar
97. Lim RG, et al. Huntington disease oligodendrocyte maturation deficits revealed by single-nucleus RNAseq are rescued by thiamine-biotin supplementation. Nat Commun. 2022;13(1):7791.
    View this article via: CrossRef PubMed Google Scholar
98. Lee H, et al. Cell type-specific transcriptomics reveals that mutant huntingtin leads to mitochondrial RNA release and neuronal innate immune activation. Neuron. 2020;107(5):891–908.
    View this article via: CrossRef PubMed Google Scholar
99. Bakken TE, et al. Single-nucleus and single-cell transcriptomes compared in matched cortical cell types. PLoS One. 2018;13(12):e0209648.
    View this article via: CrossRef PubMed Google Scholar
100. Darmanis S, et al. A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci U S A. 2015;112(23):7285–7290.
     View this article via: CrossRef PubMed Google Scholar
101. van den Brink SC, et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat Methods. 2017;14(10):935–936.
     View this article via: CrossRef PubMed Google Scholar
102. Mattei D, et al. Enzymatic dissociation induces transcriptional and proteotype bias in brain cell populations. Int J Mol Sci. 2020;21(21):7944.
     View this article via: CrossRef PubMed Google Scholar
103. Lake BB, et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science. 2016;352(6293):1586–1590.
     View this article via: CrossRef PubMed Google Scholar
104. Kharchenko PV. The triumphs and limitations of computational methods for scRNA-seq. Nat Methods. 2021;18(7):723–732.
     View this article via: CrossRef PubMed Google Scholar
105. Piwecka M, et al. Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease. Nat Rev Neurol. 2023;19(6):346–362.
     View this article via: CrossRef PubMed Google Scholar
106. Cohen L, et al. Whole-transcriptome-scale and isoform-resolved spatial imaging of single cells in complex tissues [preprint]. https://doi.org/10.1101/2025.08.27.672533 Posted on bioRxiv, August 30, 2025.
107. Ling JP, et al. TDP-43 repression of nonconserved cryptic exons is compromised in ALS-FTD. Science. 2015;349(6248):650–655.
     View this article via: CrossRef PubMed Google Scholar
108. Klim JR, et al. ALS-implicated protein TDP-43 sustains levels of STMN2, a mediator of motor neuron growth and repair. Nat Neurosci. 2019;22(2):167–179.
     View this article via: CrossRef PubMed Google Scholar
109. Melamed Z, et al. Premature polyadenylation-mediated loss of stathmin-2 is a hallmark of TDP-43-dependent neurodegeneration. Nat Neurosci. 2019;22(2):180–190.
     View this article via: CrossRef PubMed Google Scholar
110. Ma XR, et al. TDP-43 represses cryptic exon inclusion in the FTD-ALS gene UNC13A. Nature. 2022;603(7899):124–130.
     View this article via: CrossRef PubMed Google Scholar
111. Brown AL, et al. TDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A. Nature. 2022;603(7899):131–137.
     View this article via: CrossRef PubMed Google Scholar
112. Zeng Y, et al. TDP-43 nuclear loss in FTD/ALS causes widespread alternative polyadenylation changes. Nat Neurosci. 2025;28(11):2180–2189.
     View this article via: CrossRef PubMed Google Scholar
113. Bryce-Smith S, et al. TDP-43 loss induces cryptic polyadenylation in ALS/FTD. Nat Neurosci. 2025;28(11):2190–2200.
     View this article via: CrossRef PubMed Google Scholar
114. Arnold FJ, et al. TDP-43 dysregulation of polyadenylation site selection is a defining feature of RNA misprocessing in amyotrophic lateral sclerosis and frontotemporal dementia. J Clin Invest. 2025;135(11):e182088.
     View this article via: JCI CrossRef PubMed Google Scholar
115. Tomita H, et al. Effect of agonal and postmortem factors on gene expression profile: quality control in microarray analyses of postmortem human brain. Biol Psychiatry. 2004;55(4):346–352.
     View this article via: CrossRef PubMed Google Scholar
116. Lipska BK, et al. Critical factors in gene expression in postmortem human brain: focus on studies in schizophrenia. Biol Psychiatry. 2006;60(6):650–658.
     View this article via: CrossRef PubMed Google Scholar
117. Ferreira PG, et al. The effects of death and post-mortem cold ischemia on human tissue transcriptomes. Nat Commun. 2018;9(1):490.
     View this article via: CrossRef PubMed Google Scholar
118. Trapnell C, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32(4):381–386.
     View this article via: CrossRef PubMed Google Scholar
119. Saelens W, et al. A comparison of single-cell trajectory inference methods. Nat Biotechnol. 2019;37(5):547–554.
     View this article via: CrossRef PubMed Google Scholar
120. Lancaster MA, et al. Cerebral organoids model human brain development and microcephaly. Nature. 2013;501(7467):373–379.
     View this article via: CrossRef PubMed Google Scholar
121. Pașca SP. The rise of three-dimensional human brain cultures. Nature. 2018;553(7689):437–445.
     View this article via: CrossRef PubMed Google Scholar
122. Eichmüller OL, Knoblich JA. Human cerebral organoids - a new tool for clinical neurology research. Nat Rev Neurol. 2022;18(11):661–680.
     View this article via: CrossRef PubMed Google Scholar
123. McConnell MJ, et al. Mosaic copy number variation in human neurons. Science. 2013;342(6158):632–637.
     View this article via: CrossRef PubMed Google Scholar
124. Zhou Z, et al. Recurrent patterns of widespread neuronal genomic damage shared by major neurodegenerative disorders [preprint]. https://doi.org/10.1101/2025.03.03.641186 Posted on bioRxiv March 8, 2025.
125. Luo C, et al. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science. 2017;357(6351):600–604.
     View this article via: CrossRef PubMed Google Scholar
126. Kaya-Okur HS, et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun. 2019;10(1):1930.
     View this article via: CrossRef PubMed Google Scholar
127. Nagano T, et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature. 2013;502(7469):59–64.
     View this article via: CrossRef PubMed Google Scholar
128. Tan L, et al. Three-dimensional genome structures of single diploid human cells. Science. 2018;361(6405):924–928.
     View this article via: CrossRef PubMed Google Scholar
129. Budnik B, et al. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol. 2018;19(1):161.
     View this article via: CrossRef PubMed Google Scholar
130. Guise AJ, et al. TDP-43-stratified single-cell proteomics of postmortem human spinal motor neurons reveals protein dynamics in amyotrophic lateral sclerosis. Cell Rep. 2024;43(1):113636.
     View this article via: CrossRef PubMed Google Scholar
131. Wu B, et al. Every cell every gene all at once: systems genetic approaches toward corticogenesis. Curr Opin Neurobiol. 2025;92:103034.
     View this article via: CrossRef PubMed Google Scholar
132. Dräger NM, et al. A CRISPRi/a platform in human iPSC-derived microglia uncovers regulators of disease states. Nat Neurosci. 2022;25(9):1149–1162.
     View this article via: CrossRef PubMed Google Scholar
133. Jin X, et al. In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes. Science. 2020;370(6520):eaaz6063.
     View this article via: CrossRef PubMed Google Scholar
134. Santinha AJ, et al. Transcriptional linkage analysis with in vivo AAV-Perturb-seq. Nature. 2023;622(7982):367–375.
     View this article via: CrossRef PubMed Google Scholar
135. Zheng X, et al. Massively parallel in vivo Perturb-seq reveals cell-type-specific transcriptional networks in cortical development. Cell. 2024;187(13):3236–3248.
     View this article via: CrossRef PubMed Google Scholar