High-throughput muscle fiber typing from RNA sequencing data

Background: Skeletal muscle fiber type distribution has implications for human health, muscle function, and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited these studies. Here, we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested. Methods: By using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22). Results: The correlation between the sequencing-based method and the other two were r(ATpas) = 0.44 [0.13-0.67], [95% CI], and r(myosin) = 0.83 [0.61-0.93], with p = 5.70 x 10(-3) and 2.00 x 10(-6), respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of similar to 10,000 paired-end reads. Conclusions: This new method (https://github.com/OlaHanssonLab/PredictFiberType) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.Peer reviewe

High-throughput muscle fiber typing from RNA sequencing data

Background Skeletal muscle fiber type distribution has implications for human health, muscle function, and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited these studies. Here, we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested. Methods By using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22). Results The correlation between the sequencing-based method and the other two were rATPas = 0.44 [0.13–0.67], [95% CI], and rmyosin = 0.83 [0.61–0.93], with p = 5.70 × 10–3 and 2.00 × 10–6, respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of ~ 10,000 paired-end reads. Conclusions This new method (https://github.com/OlaHanssonLab/PredictFiberType) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.journal articl

Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases

Self-organizing tissues resembling brain structures generated from human stem cells offer exciting possibilities to study human brain development, disease, and evolution. These 3D models are complex and can contain cells at various stages of differentiation from different brain regions. Single-cell genomic methods provide powerful approaches to explore cell composition, differentiation trajectories, and genetic perturbations in brain organoid systems. However, it remains a major challenge to understand the heterogeneity observed within and between individual organoids. Here, we develop a set of computational tools (VoxHunt) to assess brain organoid patterning, developmental state, and cell identity through comparisons to spatial and single-cell transcriptome reference datasets. We use VoxHunt to characterize and visualize cell compositions using single-cell and bulk genomic data from multiple organoid protocols modeling different brain structures. VoxHunt will be useful to assess organoid engineering protocols and to annotate cell fates that emerge in organoids during genetic and environmental perturbation experiments.ISSN:1934-5909ISSN:1875-977

Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases

Self-organizing tissues resembling brain structures generated from human stem cells offer exciting possibilities to study human brain development, disease, and evolution. These 3D models are complex and can contain cells at various stages of differentiation from different brain regions. Single-cell genomic methods provide powerful approaches to explore cell composition, differentiation trajectories, and genetic perturbations in brain organoid systems. However, it remains a major challenge to understand the heterogeneity observed within and between individual organoids. Here, we develop a set of computational tools (VoxHunt) to assess brain organoid patterning, developmental state, and cell identity through comparisons to spatial and single-cell transcriptome reference datasets. We use VoxHunt to characterize and visualize cell compositions using single-cell and bulk genomic data from multiple organoid protocols modeling different brain structures. VoxHunt will be useful to assess organoid engineering protocols and to annotate cell fates that emerge in organoids during genetic and environmental perturbation experiments.ISSN:1934-5909ISSN:1875-977

Inferring and perturbing cell fate regulomes in human cerebral organoids

Self-organizing cerebral organoids grown from pluripotent stem cells combined with single-cell genomic technologies provide opportunities to explore gene regulatory networks (GRNs) underlying human brain development. Here we acquire single-cell transcriptome and accessible chromatin profiling data over a dense time course covering multiple phases of organoid development including neuroepithelial formation, patterning, brain regionalization, and neurogenesis. We identify temporally dynamic and brain region-specific regulatory regions, and cell interaction analysis reveals emergent patterning centers associated with regionalization. We develop Pando, a flexible linear model-based framework that incorporates multi-omic data and transcription binding site predictions to infer a global GRN describing organoid development. We use pooled genetic perturbation with single-cell transcriptome readout to assess transcription factor requirement for cell fate and state regulation in organoid. We find that certain factors regulate the abundance of cell fates, whereas other factors impact neuronal cell states after differentiation. We show that the zinc finger protein GLI3 is required for cortical fate establishment in humans, recapitulating previous work performed in mammalian model systems. We measure transcriptome and chromatin accessibility in normal or GLI3-perturbed cells and identify a regulome central to the dorsoventral telencephalic fate decision. This regulome suggests that Notch effectors HES4/5 are direct GLI3 targets, which together coordinate cortex and ganglionic eminence diversification. Altogether, we provide a framework for how multi-brain region model systems and single-cell technologies can be leveraged to reconstruct human brain developmental biology

Spatial transcriptomic and single-nucleus analysis reveals heterogeneity in a gigantic single-celled syncytium

In multicellular organisms, the specification, coordination, and compartmentalization of cell types enable the formation of complex body plans. However, some eukaryotic protists such as slime molds generate diverse and complex structures while remaining in a multinucleate syncytial state. It is unknown if different regions of these giant syncytial cells have distinct transcriptional responses to environmental encounters and if nuclei within the cell diversify into heterogeneous states. Here, we performed spatial transcriptome analysis of the slime mold Physarum polycephalum in the plasmodium state under different environmental conditions and used single-nucleus RNA-sequencing to dissect gene expression heterogeneity among nuclei. Our data identifies transcriptome regionality in the organism that associates with proliferation, syncytial substructures, and localized environmental conditions. Further, we find that nuclei are heterogenous in their transcriptional profile and may process local signals within the plasmodium to coordinate cell growth, metabolism, and reproduction. To understand how nuclei variation within the syncytium compares to heterogeneity in single-nucleus cells, we analyzed states in single Physarum amoebal cells. We observed amoebal cell states at different stages of mitosis and meiosis, and identified cytokinetic features that are specific to nuclei divisions within the syncytium. Notably, we do not find evidence for predefined transcriptomic states in the amoebae that are observed in the syncytium. Our data shows that a single-celled slime mold can control its gene expression in a region-specific manner while lacking cellular compartmentalization and suggests that nuclei are mobile processors facilitating local specialized functions. More broadly, slime molds offer the extraordinary opportunity to explore how organisms can evolve regulatory mechanisms to divide labor, specialize, balance competition with cooperation, and perform other foundational principles that govern the logic of life.ISSN:2050-084

Inferring and perturbing cell fate regulomes in human brain organoids

Self-organizing neural organoids grown from pluripotent stem cells combined with single-cell genomic technologies provide opportunities to examine gene regulatory networks underlying human brain development. Here we acquire single-cell transcriptome and accessible chromatin data over a dense time course in human organoids covering neuroepithelial formation, patterning, brain regionalization and neurogenesis, and identify temporally dynamic and brain-region-specific regulatory regions. We developed Pando—a flexible framework that incorporates multi-omic data and predictions of transcription-factor-binding sites to infer a global gene regulatory network describing organoid development. We use pooled genetic perturbation with single-cell transcriptome readout to assess transcription factor requirement for cell fate and state regulation in organoids. We find that certain factors regulate the abundance of cell fates, whereas other factors affect neuronal cell states after differentiation. We show that the transcription factor GLI3 is required for cortical fate establishment in humans, recapitulating previous research performed in mammalian model systems. We measure transcriptome and chromatin accessibility in normal or GLI3-perturbed cells and identify two distinct GLI3 regulomes that are central to telencephalic fate decisions: one regulating dorsoventral patterning with HES4/5 as direct GLI3 targets, and one controlling ganglionic eminence diversification later in development. Together, we provide a framework for how human model systems and single-cell technologies can be leveraged to reconstruct human developmental biology.ISSN:0028-0836ISSN:1476-468

Lineage recording reveals dynamics of cerebral organoid regionalization

Diverse regions develop within cerebral organoids generated from human induced pluripotent stem cells (iPSCs), however it has been a challenge to understand the lineage dynamics associated with brain regionalization. Here we establish an inducible lineage recording system that couples reporter barcodes, inducible CRISPR/Cas9 scarring, and single-cell transcriptomics to analyze lineage relationships during cerebral organoid development. We infer fate-mapped whole organoid phylogenies over a scarring time course, and reconstruct progenitor-neuron lineage trees within microdissected cerebral organoid regions. We observe increased fate restriction over time, and find that iPSC clones used to initiate organoids tend to accumulate in distinct brain regions. We use lineage-coupled spatial transcriptomics to resolve lineage locations as well as confirm clonal enrichment in distinctly patterned brain regions. Using long term 4-D light sheet microscopy to temporally track nuclei in developing cerebral organoids, we link brain region clone enrichment to positions in the neuroectoderm, followed by local proliferation with limited migration during neuroepithelial formation. Our data sheds light on how lineages are established during brain organoid regionalization, and our techniques can be adapted in any iPSC-derived cell culture system to dissect lineage alterations during perturbation or in patient-specific models of disease

NGN2 induces diverse neuron types from human pluripotency

Human neurons engineered from induced pluripotent stem cells (iPSCs) through neurogenin 2 (NGN2) overexpression are widely used to study neuronal differentiation mechanisms and to model neurological diseases. However, the differentiation paths and heterogeneity of emerged neurons have not been fully explored. Here, we used single-cell transcriptomics to dissect the cell states that emerge during NGN2 overexpression across a time course from pluripotency to neuron functional maturation. We find a substantial molecular heterogeneity in the neuron types generated, with at least two populations that express genes associated with neurons of the peripheral nervous system. Neuron heterogeneity is observed across multiple iPSC clones and lines from different individuals. We find that neuron fate acquisition is sensitive to NGN2 expression level and the duration of NGN2-forced expression. Our data reveal that NGN2 dosage can regulate neuron fate acquisition, and that NGN2-iN heterogeneity can confound results that are sensitive to neuron type.ISSN:2213-671

Human neuron subtype programming through combinatorial patterning with scRNA-seq readouts

Human neurons programmed through transcription factor (TF) overexpression model neuronal differentiation and neurological diseases. However, programming specific neuron types remains challenging. Here, we modulate developmental signaling pathways combined with TF overexpression to explore the spectrum of neuron subtypes generated from pluripotent stem cells. We screened 480 morphogen signaling modulations coupled with NGN2 or ASCL1/DLX2 induction using a multiplexed single-cell transcriptomic readout. Analysis of 700,000 cells identified diverse excitatory and inhibitory neurons patterned along the anterior-posterior and dorsal-ventral axes of neural tube development. We inferred signaling and TF interaction networks guiding differentiation of forebrain, midbrain, hindbrain, spinal cord, peripheral sympathetic and sensory neurons. Our approach provides a strategy for cell subtype programming and to investigate how cooperative signaling drives neuronal fate