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1.
Nature ; 560(7719): 494-498, 2018 08.
Artigo em Inglês | MEDLINE | ID: mdl-30089906

RESUMO

RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity-the time derivative of the gene expression state-can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.


Assuntos
Encéfalo/citologia , Crista Neural/metabolismo , Neurônios/citologia , Splicing de RNA/genética , RNA/análise , RNA/genética , Análise de Sequência de RNA , Análise de Célula Única , Animais , Encéfalo/embriologia , Encéfalo/metabolismo , Linhagem da Célula/genética , Células Cromafins/citologia , Células Cromafins/metabolismo , Conjuntos de Dados como Assunto , Feminino , Ácido Glutâmico/metabolismo , Hipocampo/citologia , Hipocampo/embriologia , Hipocampo/metabolismo , Cinética , Masculino , Camundongos , Crista Neural/citologia , Neurônios/metabolismo , Reprodutibilidade dos Testes , Fatores de Tempo , Transcrição Gênica/genética
2.
Nat Methods ; 16(8): 695-698, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31308548

RESUMO

Single-cell RNA sequencing is often applied in study designs that include multiple individuals, conditions or tissues. To identify recurrent cell subpopulations in such heterogeneous collections, we developed Conos, an approach that relies on multiple plausible inter-sample mappings to construct a global graph connecting all measured cells. The graph enables identification of recurrent cell clusters and propagation of information between datasets in multi-sample or atlas-scale collections.


Assuntos
Medula Óssea/metabolismo , Biologia Computacional/métodos , Bases de Dados Genéticas , Perfilação da Expressão Gênica , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Análise de Célula Única/métodos , Humanos
3.
Genome Biol ; 25(1): 35, 2024 01 25.
Artigo em Inglês | MEDLINE | ID: mdl-38273415

RESUMO

Targeted spatial transcriptomics hold particular promise in analyzing complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is their reliance on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method performing gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.


Assuntos
Perfilação da Expressão Gênica , Técnicas Genéticas , Análise de Célula Única , Transcriptoma , Análise de Sequência de RNA
4.
bioRxiv ; 2024 Mar 13.
Artigo em Inglês | MEDLINE | ID: mdl-38559123

RESUMO

Recently, single-cell RNA-sequencing (scRNA-seq) has enabled unprecedented insights to the cellular landscape of the brains of many different species, among them the rhesus macaque as a key animal model. Building on previous, broader surveys of the macaque brain, we closely examined five immediately neighboring areas within the visual cortex of the rhesus macaque: V1, V2, V4, MT and TEO. To facilitate this, we first devised a novel pipeline for brain spatial archive - the BrainSPACE - which enabled robust archiving and sampling from the whole unfixed brain. SnRNA-sequencing of ~100,000 nuclei from visual areas V1 and V4 revealed conservation within the GABAergic neuron subtypes, while seven and one distinct principle neuron subtypes were detected in V1 and V4, respectively, all most likely located in layer 4. Moreover, using small molecule fluorescence in situ hybridization, we identified cell type density gradients across V1, V2, V4, MT, and TEO appearing to reflect the visual hierarchy. These findings demonstrate an association between the clear areal specializations among neighboring areas with the hierarchical levels within the visual cortex of the rhesus macaque.

5.
bioRxiv ; 2023 Mar 24.
Artigo em Inglês | MEDLINE | ID: mdl-36993340

RESUMO

Targeted spatial transcriptomics hold particular promise in analysis of complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is that they rely on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method to perform gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.

6.
Sci Rep ; 13(1): 9567, 2023 06 13.
Artigo em Inglês | MEDLINE | ID: mdl-37311768

RESUMO

With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and gene expression information about cells in tissue sections at single cell resolution. Cell type classification of these spatially-resolved cells can be inferred by matching the spatial transcriptomics data to reference atlases derived from single cell RNA-sequencing (scRNA-seq) in which cell types are defined by differences in their gene expression profiles. However, robust cell type matching of the spatially-resolved cells to reference scRNA-seq atlases is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four image-based spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that many cells are assigned as the same type by multiple cell type matching algorithms and are present in spatial patterns previously reported from scRNA-seq studies in VISp. Furthermore, by combining the results of individual matching strategies into consensus cell type assignments, we see even greater alignment with biological expectations. We present two ensemble meta-analysis strategies used in this study and share the consensus cell type matching results in the Cytosplore Viewer ( https://viewer.cytosplore.org ) for interactive visualization and data exploration. The consensus matching can also guide spatial data analysis using SSAM, allowing segmentation-free cell type assignment.


Assuntos
Córtex Visual Primário , Transcriptoma , Animais , Camundongos , Hibridização in Situ Fluorescente , Perfilação da Expressão Gênica , Algoritmos
7.
Mol Brain ; 15(1): 45, 2022 05 16.
Artigo em Inglês | MEDLINE | ID: mdl-35578248

RESUMO

Synaptic vesicle glycoprotein-2 (SV2) is a family of proteins consisting of SV2A, SV2B, and SV2C. This protein family has attracted attention in recent years after SV2A was shown to be an epileptic drug target and a perhaps a biomarker of synaptic density. So far, the anatomical localization of these proteins in the rodent and human brain have been reported, but co-expression of SV2 genes on a cellular level, their expressions in the human brain, comparison to radioligand binding, any possible regulation in epilepsy are not known. We have here analyzed the expression of SV2 genes in neuronal subtypes in the temporal neocortex in selected specimens by using single nucleus-RNA sequencing, and performed quantitative PCR in populations of temporal lobe epilepsy (TLE) patients and healthy controls. [3H]-UCB-J autoradiography was performed to analyze the correlation between the mRNA transcript and binding capacity to SV2A. Our data showed that the SV2A transcript is expressed in all glutamatergic and GABAergic cortical subtypes, while SV2B expression is restricted to only the glutamatergic neurons and SV2C has very limited expression in a small subgroup of GABAergic interneurons. The level of [3H]-UCB-J binding and the concentration of SV2A mRNA is strongly correlated in each patient, and the expression is lower in the TLE patients. There is no relationship between SV2A expression and age, sex, seizure frequency, duration of epilepsy, or whether patients were recently treated with levetiracetam or not. Collectively, these findings point out a neuronal subtype-specific distribution of the expression of the three SV2 genes, and the lower levels of both radioligand binding and expression further emphasize the significance of these proteins in this disease.


Assuntos
Epilepsia do Lobo Temporal , Epilepsia , Neocórtex , Epilepsia/genética , Epilepsia do Lobo Temporal/genética , Humanos , Glicoproteínas de Membrana/genética , Glicoproteínas de Membrana/metabolismo , Neocórtex/metabolismo , Proteínas do Tecido Nervoso/genética , Proteínas do Tecido Nervoso/metabolismo , RNA Mensageiro/genética , Vesículas Sinápticas/metabolismo
8.
Nat Biotechnol ; 40(3): 345-354, 2022 03.
Artigo em Inglês | MEDLINE | ID: mdl-34650268

RESUMO

Single-molecule spatial transcriptomics protocols based on in situ sequencing or multiplexed RNA fluorescent hybridization can reveal detailed tissue organization. However, distinguishing the boundaries of individual cells in such data is challenging and can hamper downstream analysis. Current methods generally approximate cells positions using nuclei stains. We describe a segmentation method, Baysor, that optimizes two-dimensional (2D) or three-dimensional (3D) cell boundaries considering joint likelihood of transcriptional composition and cell morphology. While Baysor can take into account segmentation based on co-stains, it can also perform segmentation based on the detected transcripts alone. To evaluate performance, we extend multiplexed error-robust fluorescence in situ hybridization (MERFISH) to incorporate immunostaining of cell boundaries. Using this and other benchmarks, we show that Baysor segmentation can, in some cases, nearly double the number of cells compared to existing tools while reducing segmentation artifacts. We demonstrate that Baysor performs well on data acquired using five different protocols, making it a useful general tool for analysis of imaging-based spatial transcriptomics.


Assuntos
Análise de Célula Única , Transcriptoma , Perfilação da Expressão Gênica/métodos , Hibridização in Situ Fluorescente/métodos , RNA/análise , Análise de Célula Única/métodos , Transcriptoma/genética
9.
Sci Adv ; 8(41): eabn8367, 2022 10 14.
Artigo em Inglês | MEDLINE | ID: mdl-36223459

RESUMO

Schizophrenia is one of the most widespread and complex mental disorders. To characterize the impact of schizophrenia, we performed single-nucleus RNA sequencing (snRNA-seq) of >220,000 neurons from the dorsolateral prefrontal cortex of patients with schizophrenia and matched controls. In addition, >115,000 neurons were analyzed topographically by immunohistochemistry. Compositional analysis of snRNA-seq data revealed a reduction in abundance of GABAergic neurons and a concomitant increase in principal neurons, most pronounced for upper cortical layer subtypes, which was substantiated by histological analysis. Many neuronal subtypes showed extensive transcriptomic changes, the most marked in upper-layer GABAergic neurons, including down-regulation in energy metabolism and up-regulation in neurotransmission. Transcription factor network analysis demonstrated a developmental origin of transcriptomic changes. Last, Visium spatial transcriptomics further corroborated upper-layer neuron vulnerability in schizophrenia. Overall, our results point toward general network impairment within upper cortical layers as a core substrate associated with schizophrenia symptomatology.


Assuntos
Esquizofrenia , Neurônios GABAérgicos/metabolismo , Humanos , Córtex Pré-Frontal/metabolismo , RNA Nuclear Pequeno/metabolismo , Esquizofrenia/patologia , Fatores de Transcrição/metabolismo
11.
Nat Commun ; 11(1): 5038, 2020 10 07.
Artigo em Inglês | MEDLINE | ID: mdl-33028830

RESUMO

Epilepsy is one of the most common neurological disorders, yet its pathophysiology is poorly understood due to the high complexity of affected neuronal circuits. To identify dysfunctional neuronal subtypes underlying seizure activity in the human brain, we have performed single-nucleus transcriptomics analysis of >110,000 neuronal transcriptomes derived from temporal cortex samples of multiple temporal lobe epilepsy and non-epileptic subjects. We found that the largest transcriptomic changes occur in distinct neuronal subtypes from several families of principal neurons (L5-6_Fezf2 and L2-3_Cux2) and GABAergic interneurons (Sst and Pvalb), whereas other subtypes in the same families were less affected. Furthermore, the subtypes with the largest epilepsy-related transcriptomic changes may belong to the same circuit, since we observed coordinated transcriptomic shifts across these subtypes. Glutamate signaling exhibited one of the strongest dysregulations in epilepsy, highlighted by layer-wise transcriptional changes in multiple glutamate receptor genes and strong upregulation of genes coding for AMPA receptor auxiliary subunits. Overall, our data reveal a neuronal subtype-specific molecular phenotype of epilepsy.


Assuntos
Epilepsia Resistente a Medicamentos/genética , Epilepsia do Lobo Temporal/genética , Neurônios/patologia , Lobo Temporal/patologia , Transcriptoma/genética , Adolescente , Adulto , Biópsia , Estudos de Casos e Controles , Núcleo Celular/genética , Núcleo Celular/metabolismo , Conjuntos de Dados como Assunto , Epilepsia Resistente a Medicamentos/diagnóstico , Epilepsia Resistente a Medicamentos/patologia , Epilepsia Resistente a Medicamentos/cirurgia , Epilepsia do Lobo Temporal/diagnóstico , Epilepsia do Lobo Temporal/patologia , Epilepsia do Lobo Temporal/cirurgia , Feminino , Ácido Glutâmico/metabolismo , Humanos , Imageamento por Ressonância Magnética , Masculino , Microdissecção , Pessoa de Meia-Idade , Modelos Genéticos , Rede Nervosa/metabolismo , Rede Nervosa/patologia , Neurônios/citologia , Neurônios/metabolismo , RNA-Seq , Receptores de AMPA/genética , Receptores de AMPA/metabolismo , Receptores de Glutamato/genética , Receptores de Glutamato/metabolismo , Transdução de Sinais/genética , Análise de Célula Única , Lobo Temporal/citologia , Lobo Temporal/diagnóstico por imagem , Lobo Temporal/cirurgia , Transcrição Gênica , Regulação para Cima , Adulto Jovem
12.
Genome Biol ; 19(1): 78, 2018 06 19.
Artigo em Inglês | MEDLINE | ID: mdl-29921301

RESUMO

Recent single-cell RNA-seq protocols based on droplet microfluidics use massively multiplexed barcoding to enable simultaneous measurements of transcriptomes for thousands of individual cells. The increasing complexity of such data creates challenges for subsequent computational processing and troubleshooting of these experiments, with few software options currently available. Here, we describe a flexible pipeline for processing droplet-based transcriptome data that implements barcode corrections, classification of cell quality, and diagnostic information about the droplet libraries. We introduce advanced methods for correcting composition bias and sequencing errors affecting cellular and molecular barcodes to provide more accurate estimates of molecular counts in individual cells.


Assuntos
Código de Barras de DNA Taxonômico/métodos , RNA/genética , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodos , Animais , Linhagem Celular Tumoral , Perfilação da Expressão Gênica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Humanos , Células K562 , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Microfluídica/métodos , Software , Transcriptoma/genética
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