in silico transcriptome dissection of neocortical excitatory neurogenesis via joint matrix decomposition and transfer learning.
in silico transcriptome dissection of neocortical excitatory neurogenesis via joint matrix decomposition and transfer learning.
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通过关节矩阵分解和迁移学习对新皮质兴奋性神经发生进行计算机转录组解剖。
DOI：
10.1101/2024.02.26.581612
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Sestan
中科院分区：
文献类型：
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作者：
Sonthalia,Shreyash;Li,Guangyan;Blanco,XoelMato;Casella,Alex;Liu,Jinrui;Stein-O'Brien,Genevieve;Caffo,Brian;Adkins,RickyS;Orvis,Joshua;Hertzano,Ronna;Mahurkar,Anup;Gillis,Jesse;Werner,Jonathan;Ma,Shaojie;Micali,Nicola;Sestan
已经产生了大量的多组学数据来表征人类和其他哺乳动物大脑皮层细胞类型的发育和多样性。为了更充分地利用这些数据的集体发现潜力，我们收集了来自 188 项已发表的新皮质发育研究的基因级转录组数据，包括约 3000 万个单细胞的转录组、广泛的空间转录组实验以及分选细胞和大块组织的 RNA 测序：nemoanalytics.org/landing/neocortex。通过将联合矩阵分解（SJD）应用于该集合中的小鼠、猕猴和人类数据，我们定义了在哺乳动物神经发生过程中保守的转录组动力学，并阐明了外部或基底放射状胶质细胞的进化。成人新皮质数据的分解确定了成熟神经元的层特异性特征，并与 NeMO Analytics 中的迁移学习方法相结合，能够绘制出其早期发育出现和出生后数年的长期成熟的图表。对大脑类器官数据的询问表明，虽然体内发育的广泛分子要素在体外得到重现，但神经元成熟中的许多层特异性转录组程序却缺失。我们邀请没有编码专业知识的计算生物学家和细胞生物学家在他们的研究中使用 NeMO Analytics，并用新兴数据为其提供动力 (carlocolantuoni.org)。
Vast quantities of multi-omic data have been produced to characterize the development and diversity of cell types in the cerebral cortex of humans and other mammals. To more fully harness the collective discovery potential of these data, we have assembled gene-level transcriptomic data from 188 published studies of neocortical development, including the transcriptomes of ~30 million single-cells, extensive spatial transcriptomic experiments and RNA sequencing of sorted cells and bulk tissues: nemoanalytics.org/landing/neocortex. Applying joint matrix decomposition (SJD) to mouse, macaque and human data in this collection, we defined transcriptome dynamics that are conserved across mammalian neurogenesis and which elucidate the evolution of outer, or basal, radial glial cells. Decomposition of adult human neocortical data identified layer-specific signatures in mature neurons and, in combination with transfer learning methods in NeMO Analytics, enabled the charting of their early developmental emergence and protracted maturation across years of postnatal life. Interrogation of data from cerebral organoids demonstrated that while broad molecular elements of in vivo development are recapitulated in vitro, many layer-specific transcriptomic programs in neuronal maturation are absent. We invite computational biologists and cell biologists without coding expertise to use NeMO Analytics in their research and to fuel it with emerging data (carlocolantuoni.org).