Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes

Humphrey, Jack; Venkatesh, Sanan; Hasan, Rahat; Herb, Jake T.; Lopes, Kátia de Paiva; Küçükali, Fahri; Byrska-Bishop, Marta; Evani, Uday Shankar; Narzisi, Giuseppe; Fagegaltier, Delphine; Sleegers, Kristel; Phatnani, Hemali; Knowles, David A.; Fratta, Pietro; Raj, Towfique

doi:10.1038/s41593-022-01205-3

Cited by 58 publications

(85 citation statements)

References 107 publications

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“…Despite evidence of microglia showing traits of neuroprotective and dysfunctional phenotypes, it remains possible that microglia acquire a neurotoxic phenotype in response to pTDP-43; a neurotoxic microglial phenotype has been induced in vitro in response to both native and mutant TDP-43 species [9]. Recent transcriptomic analyses of the human ALS spinal cord demonstrate that certain microglial functional states negatively correlated with disease duration, which was suggested to be evidence of microglial neurotoxicity in ALS [10]. However, the microglial state that negatively correlated with disease progression showed high expression of CD68 and FTL (L-ferritin) [10].…”

Section: Discussionmentioning

confidence: 99%

“…Recent transcriptomic analyses of the human ALS spinal cord demonstrate that certain microglial functional states negatively correlated with disease duration, which was suggested to be evidence of microglial neurotoxicity in ALS [10]. However, the microglial state that negatively correlated with disease progression showed high expression of CD68 and FTL (L-ferritin) [10]. While this could be a neurotoxic microglial state, we posit that, like in Alzheimer’s disease, this L-ferritin high population is dysfunctional.…”

Section: Discussionmentioning

confidence: 99%

“…A neurotoxic microglial phenotype can be induced by native and mutant forms of TDP-43 in vitro [9], demonstrating the capacity for a microglial functional shift in response to TDP-43. Furthermore, specific microglial states present in the human ALS spinal cord containing pTDP-43 pathology negatively correlated with disease progression [10]. In contrast, the nuclear-localisation sequence-defective TDP-43 mouse model of ALS demonstrated microglia express unique suites of genes at each disease state, with strong neuroprotective gene signatures being identified at early- and late-stage disease, and recovery, arguing against a late-stage neurotoxic microglial phenotype [11].…”

Section: Introductionmentioning

confidence: 99%

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Microglial CD68 and L-ferritin upregulation in response to phosphorylated-TDP-43 pathology in the amyotrophic lateral sclerosis brain

Swanson

Mrkela

Murray

et al. 2023

Preprint

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Microglia, the innate immune cells of the brain, are activated by damage or disease. In mouse models of amyotrophic lateral sclerosis (ALS), microglia shift from neurotrophic to neurotoxic states with disease progression. It remains unclear how human microglia change relative to the TAR DNA-binding protein 43 (TDP-43) aggregation that occurs in 97% of ALS cases. Here we examine spatial relationships between microglial activation and TDP-43 pathology in brain tissue from people with ALS and from a TDP-43-driven ALS mouse model. Post-mortem human brain tissue from the Neurological Foundation Human Brain Bank was obtained from 10 control and 10 ALS cases in parallel with brain tissue from a bigenic NEFH-tTA/tetO-hTDP-43deltaNLS (rNLS) mouse model of ALS at disease onset, early disease, and late disease stages. The spatiotemporal relationship between microglial activation and ALS pathology was determined by investigating microglial functional marker expression in brain regions with low and high TDP-43 burden at end-stage human disease: hippocampus and motor cortex, respectively. Sections were immunohistochemically labelled with a two-round multiplexed antibody panel against; microglial functional markers (L-ferritin, HLA-DR, CD74, CD68, and Iba1), a neuronal marker (NeuN), an astrocyte marker (GFAP), and pathological phosphorylated TDP-43 (pTDP-43). Single-cell levels of microglial functional markers were quantified using custom analysis pipelines and mapped to anatomical regions and ALS pathology. We identified a significant increase in microglial Iba1 and CD68 expression in the human ALS motor cortex, with microglial CD68 being significantly correlated with pTDP-43 pathology load. We also identified two subpopulations of microglia enriched in the ALS motor cortex that were defined by high L-ferritin expression. A similar pattern of microglial changes was observed in the rNLS mouse, with an increase first in CD68 and then in L-ferritin expression, with both occurring only after pTDP-43 inclusions were detectable. Our data strongly suggest that microglia are phagocytic at early-stage ALS but transition to a dysfunctional state at end-stage disease, and that these functional states are driven by pTDP-43 aggregation. Overall, these findings enhance our understanding of microglial phenotypes and function in ALS.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Microglial CD68 and L-ferritin upregulation in response to phosphorylated-TDP-43 pathology in the amyotrophic lateral sclerosis brain

Swanson

Mrkela

Murray

et al. 2023

Preprint

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“…3B). In a second layer of analysis, we applied our deep learning method to an independent transcriptome originating from the spinal cord of ALS patients (GSE137810) 36 (Fig. 3C), which also highlighted a downregulation of 141 synaptic transcripts that, according to the SynGO database 37 , were significantly associated with pre-and postsynaptic terms, including synaptic vesicles and neurotransmitter release (Supplementary Fig.…”

Section: Deep Machine-learning Defines An Als Transcriptional Portrai...mentioning

confidence: 99%

Multiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis

Catanese

Rajkumar

Sommer

et al. 2023

Brain

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Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease that mainly affects the neurons of the motor system. Despite the increasing understanding of its genetic components, their biological meanings are still poorly understood. Indeed, it is still not clear to which extent the pathological features associated with ALS are commonly shared by the different genes causally linked to this disorder. To address this point, we combined multi-omics analysis covering the transcriptional, epigenetic and mutational aspects of heterogenous hiPSC-derived C9orf72-, TARDBP-, SOD1- and FUS-mutant motor neurons as well as datasets from patients’ biopsies. We identified a common signature, converging toward increased stress and synaptic abnormalities, which reflects a unifying transcriptional program in ALS despite the specific profiles owing to the underlying pathogenic gene. In addition, whole genome bisulfite sequencing linked the altered gene expression observed in mutant cells to their methylation profile, highlighting deep epigenetic alterations as part of the abnormal transcriptional signatures linked to ALS. We then applied multi-layer deep machine-learning to integrate publicly-available blood and spinal cord transcriptomes and found a statistically significant correlation between their top predictor gene sets, which were significantly enriched in toll-like receptor signaling. Notably, the overrepresentation of this biological term also correlated with the transcriptional signature identified in mutant hiPSC-derived motor neurons, highlighting novel insights into ALS marker genes in a tissue-independent manner. Finally, using whole genome sequencing in combination with deep learning, we generated the first mutational signature for ALS and defined a specific genomic profile for this disease, which is significantly correlated to aging signatures, hinting at age as a major player in ALS. All in all, this work describes innovative methodological approaches for the identification of disease signatures through the combination of multi-omics analysis and provides novel knowledge on the pathological convergencies defining ALS.

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“…In this cluster, there was clear involvement of the major histocompatibility complex class II and the HLA complex (HLA-DRA, HLA-DMB, HLA-DOA, HLA-DPA1, HLA-DRB1, HLA-DRB5, HLA-DRB6), M1 or activated microglia (CD14, CD86, TREM2, TYROBP, TMEM119, TMEM125) 37 and proinflammatory metalloproteinases (MMP14), as well as many immune related genes which were identified in other motor cortex and spinal cord SALS expression studies 8,43,44 . Three tentative ALSrelated modifier genes (LUM 45 , LIF 46 , CX3CR1 47 ), which are involved in proinflammatory processes [48][49][50] and microglial-induced neuronal cell loss 51 , were also present in this cluster.…”

Section: Cluster 3 -Inflammationmentioning

confidence: 99%

Unsupervised machine learning identifies distinct molecular and phenotypic ALS subtypes in post-mortem motor cortex and blood expression data

Marriott

Kabiljo

Hunt

et al. 2023

Preprint

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Background: Amyotrophic lateral sclerosis (ALS) displays considerable clinical, genetic and molecular heterogeneity. Machine learning approaches have shown potential to disentangle complex disease landscapes and they have been utilised for patient stratification in ALS. However, lack of independent validation in different populations and in pre-mortem tissue samples have greatly limited their use in clinical and research settings. We overcame such issues by performing a large-scale study of over 600 post-mortem brain and blood samples of people with ALS from four independent datasets from the UK, Italy, the Netherlands and the US. Methods: Hierarchical clustering was performed on the 5000 most variably expressed autosomal genes identified from post-mortem motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N=112). The molecular architectures of each cluster were investigated with gene enrichment, network and cell composition analysis. Methylation and genetic data were also used to assess if other omics measures differed between individuals. Validation of these clusters was achieved by applying linear discriminant analysis models based on the KCL BrainBank to the TargetALS US motor cortex (N=93), as well as Italian (N=15) and Dutch (N=397) blood expression datasets. Phenotype analysis was also performed to assess cluster-specific differences in clinical outcomes. Results: We identified three molecular phenotypes, which reflect the proposed major mechanisms of ALS pathogenesis: synaptic and neuropeptide signalling, excitotoxicity and oxidative stress, and neuroinflammation. Known ALS risk genes were identified among the informative genes of each cluster, suggesting potential for genetic profiling of the molecular phenotypes. Cell types which are known to be associated with specific molecular phenotypes were found in higher proportions in those clusters. These molecular phenotypes were validated in independent motor cortex and blood datasets. Phenotype analysis identified distinct cluster-related outcomes associated with progression, survival and age of death. We developed a public webserver (https://alsgeclustering.er.kcl.ac.uk) that allows users to stratify samples with our model by uploading their expression data. Conclusions: We have identified three molecular phenotypes, driven by different cell types, which reflect the proposed major mechanisms of ALS pathogenesis. Our results support the hypothesis of biological heterogeneity in ALS where different mechanisms underly ALS pathogenesis in a subgroup of patients that can be identified by a specific expression signature. These molecular phenotypes show potential for stratification of clinical trials, the development of biomarkers and personalised treatment approaches.

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Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes

Cited by 58 publications

References 107 publications

Microglial CD68 and L-ferritin upregulation in response to phosphorylated-TDP-43 pathology in the amyotrophic lateral sclerosis brain

Microglial CD68 and L-ferritin upregulation in response to phosphorylated-TDP-43 pathology in the amyotrophic lateral sclerosis brain

Multiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis

Unsupervised machine learning identifies distinct molecular and phenotypic ALS subtypes in post-mortem motor cortex and blood expression data

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