Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq

By C. Cadwell, Athanasia G. Palasantza, Xiaolong Jiang, Philipp Berens, Q. Deng, M. Yilmaz, Jacob Reimer, Shan Shen, Matthias Bethge, K. Tolias and 2 more. 2015

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Inverse statistical problems: from the inverse Ising problem to data science (2017, methodology): “Such an approach has already been used in gene regulatory networks [172, 235], and with the expansion of tools to analyze large numbers of single cells [42, 230], it may soon spread to other types of networks.”
Sparse activity of identified dentate granule cells during spatial exploration (2016, methodology): “Sparse logistic regression classifier To build an automatic cell-type classifier that uses morphological parameters, we trained an L1-regularized logistic regression classifier (Friedman et al., 2010) using nested cross-validation (Cadwell et al., 2016).”
Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics (2017, unknown): “Patch-seq analysis confirms that genetic continuum correlates with fast-spiking phenotype To test the hypothesis that the latent factor correlates with a neuron’s electrophysiological firing pattern, we turned to Patch-seq analysis (Cadwell et al., 2016; Fuzik et al., 2016).”
Deep learning and alignment of spatially-resolved single cell transcriptomes with Tangram (2021, unknown): “This approach can be adopted with other multimodal single-cell methods (for example, cellular indexing of transcriptomes and epitopes by sequencing 40 and Patch-seq 41 ) or with independently measured single-cell modalities integrated in a common latent space 24,42,43 .”