@article {70120,
	title = {Establishing a continuum of cell types in the visual cortex},
	journal = {bioRxiv},
	year = {2025},
	month = {2025 Sep 22},
	abstract = {<p>The mammalian cerebral cortex is composed of neurons whose properties vary in a continuous fashion rather than falling into discrete cell types. In the mouse visual cortex, excitatory neurons in layer 2 and 3 (L2/3) form such a continuum along cortical depth, patterned by the graded expression of hundreds of genes. Here we sought to understand how this continuum develops and contributes to cortical wiring. Using single-nucleus multiomics (RNA- and ATAC-Seq) and spatial transcriptomics, we show that the L2/3 continuum is established in two phases. During the first postnatal week, a genetically hardwired program establishes a primitive continuum of cell identities spanning the depth of L2/3. The second program, promoted by visual experience, is later superimposed upon the preexisting continuum. This second phase is driven by activity-regulated transcription factors that drive the L2/3 depth-dependent expression of genes linked to synaptic function and plasticity. We show that neurons at different positions along the L2/3 continuum project preferentially to distinct higher visual areas and that visual deprivation disrupts targeting to some higher visual areas while sparing others. Thus, cortical continua emerge through a stepwise process in which genetic programs and sensory experience specify neuronal identity and sculpt intracortical wiring specificity.</p>
},
	doi = {10.1101/2025.09.22.677893},
	url = {https://www.biorxiv.org/content/early/2025/09/22/2025.09.22.677893},
	author = {Yoo, Juyoun and Xie, Fangming and Butrus, Salwan and Xu, Runzhe and Tan, Zhiqun and Gorzek, Ryan and Mirshahidi, Parmis and Tring, Elaine and Suresh, Sanjana and Kim, Jinho and Fleishman, Greg and Tan, Liming and Ringach, Dario and Trachtenberg, Joshua and Xu, Xiangmin and Zipursky, S. Lawrence and Shekhar, Karthik and Jain, Saumya}
}
@article {65177,
	title = {Identification of cell types from single-cell transcriptomic data.},
	journal = {Methods in Molecular Biology},
	volume = {1935},
	year = {2019},
	month = {2019 Oct 16},
	pages = {45-77},
	abstract = {<p>Unprecedented technological advances in single-cell RNA-sequencing (scRNA-seq) technology have now made it possible to profile genome-wide expression in single cells at low cost and high throughput. There is substantial ongoing effort to use scRNA-seq measurements to identify the "cell types" that form components of a complex tissue, akin to taxonomizing species in ecology. Cell type classification from scRNA-seq data involves the application of computational tools rooted in dimensionality reduction and clustering, and statistical analysis to identify molecular signatures that are unique to each type. As datasets continue to grow in size and complexity, computational challenges abound, requiring analytical methods to be scalable, flexible, and robust. Moreover, careful consideration needs to be paid to experimental biases and statistical challenges that are unique to these measurements to avoid artifacts. This chapter introduces these topics in the context of cell-type identification, and outlines an instructive step-by-step example bioinformatic pipeline for researchers entering this field.</p>
},
	keywords = {Animals, Cluster Analysis, Computational Biology, Gene Expression Profiling, Genome, High-Throughput Nucleotide Sequencing, Humans, Mice, RNA, Sequence Analysis, RNA, Single-Cell Analysis, Transcriptome},
	issn = {1940-6029},
	doi = {10.1007/978-1-4939-9057-3_4},
	author = {Shekhar, Karthik and Menon, Vilas}
}
