Combinatorial quantification of distinct neural projections from retrograde tracing

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Ноя. 10, 2023

Abstract Comprehensive quantification of neuronal architectures underlying anatomical brain connectivity remains challenging. We introduce a method to identify distinct axonal projection patterns from source set target regions and the count neurons with each pattern. A region projecting n targets could have 2 -1 theoretically possible types, although only subset these types typically exists. By injecting uniquely labeled retrograde tracers in k ( < ), one can experimentally cells expressing different color combinations region. The counts for -choose- experiments provide constraints model that is robustly solvable using evolutionary algorithms. Here, we demonstrate this method’s reliability 4 simulated triple injection experiments. Furthermore, illustrate experimental application framework by quantifying projections male mouse primary motor cortex secondary somatosensory cortices.

Язык: Английский

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A novel method for clustering cellular data to improve classification

Neural Regeneration Research, Год журнала: 2024, Номер 20(9), С. 2697 - 2705

Опубликована: Сен. 24, 2024

Many fields, such as neuroscience, are experiencing the vast proliferation of cellular data, underscoring need for organizing and interpreting large datasets. A popular approach partitions data into manageable subsets via hierarchical clustering, but objective methods to determine appropriate classification granularity missing. We recently introduced a technique systematically identify when stop subdividing clusters based on fundamental principle that cells must differ more between than within clusters. Here we present corresponding protocol classify datasets by combining data-driven unsupervised clustering with statistical testing. These general-purpose functions applicable any dataset can be organized two-dimensional matrices numerical values, including molecular, physiological, anatomical demonstrate using from Janelia MouseLight project characterize morphological aspects neurons.

Язык: Английский

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Where do cell states end and cell types begin?

The Transmitter, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Язык: Английский

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Discovery of optimal cell type classification marker genes from single cell RNA sequencing data

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Апрель 27, 2024

Abstract The use of single cell/nucleus RNA sequencing (scRNA-seq) technologies that quantitively describe cell transcriptional phenotypes is revolutionizing our understanding biology, leading to new insights in type identification, disease mechanisms, and drug development. tremendous growth scRNA-seq data has posed challenges efficiently characterizing data-driven types identifying quantifiable marker genes for classification. machine learning explainable artificial intelligence emerged as an effective approach study large-scale data. NS-Forest a random forest learning-based algorithm aims provide scalable solution identify minimum combinations necessary sufficient capture identity with maximum classification accuracy. Here, we the latest version, version 4.0 its companion Python package ( https://github.com/JCVenterInstitute/NSForest ), several enhancements select gene exhibit highly selective expression patterns among closely related more perform selection atlases millions cells. By modularizing final decision tree step, v4.0 can be used compare performance user-defined computationally-derived based on classifiers. To quantify how well identified markers desired pattern being exclusively expressed at high levels within their target types, introduce On-Target Fraction metric ranges from 0 1, 1 assigned are only not cells any other types. outperforms previous versions ability higher values approaches significantly F-beta scores when applied datasets three human organs - brain, kidney, lung.

Язык: Английский

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Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

Frontiers in Molecular Neuroscience, Год журнала: 2024, Номер 17

Опубликована: Июнь 17, 2024

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics particularly urgent considering long list late-stage drug candidate failures. Although our knowledge on pathogenic mechanisms driving neurodegeneration growing, additional efforts are required to achieve better ultimately complete understanding pathophysiological underpinnings NDDs. Beyond etiology NDDs being heterogeneous multifactorial, this process further complicated by fact that current experimental models only partially recapitulate major phenotypes observed in humans. In such scenario, multi-omic approaches have potential accelerate identification new or repurposed drugs against multitude underlying One advantage for implementation these overarching tools able disentangle disease states model perturbations through comprehensive characterization distinct molecular layers (i.e., genome, transcriptome, proteome) up single-cell resolution. Because recent advances increasing their affordability scalability, use omics technologies drive nascent, but rapidly expanding neuroscience field. Combined with increasingly advanced vitro models, which benefited from introduction human iPSCs, multi-omics shaping paradigm NDDs, prediction screening. review, we discuss examples, main advantages open challenges targets therapies

Язык: Английский

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