@article {69185, title = {Brain-wide neural activity underlying memory-guided movement.}, journal = {Cell}, volume = {187}, year = {2024}, month = {2024 Feb 01}, pages = {676-691.e16}, abstract = {

Behavior relies on activity in structured neural circuits that are distributed across the brain, but most experiments probe neurons in a single area at a time. Using multiple Neuropixels probes, we recorded from multi-regional loops connected to the anterior lateral motor cortex (ALM), a circuit node mediating memory-guided directional licking. Neurons encoding sensory stimuli, choices, and actions were distributed across the brain. However, choice coding was concentrated in the ALM and subcortical areas receiving input from the ALM in an ALM-dependent manner. Diverse orofacial movements were encoded in the hindbrain; midbrain; and, to a lesser extent, forebrain. Choice signals were first detected in the ALM and the midbrain, followed by the thalamus and other brain areas. At movement initiation, choice-selective activity collapsed across the brain, followed by new activity patterns driving specific actions. Our experiments provide the foundation for neural circuit models of decision-making and movement initiation.

}, keywords = {Brain, Memory, Movement, Neurons, Thalamus}, issn = {1097-4172}, doi = {10.1016/j.cell.2023.12.035}, author = {Chen, Susu and Liu, Yi and Wang, Ziyue Aiden and Colonell, Jennifer and Liu, Liu D and Hou, Han and Tien, Nai-Wen and Wang, Tim and Harris, Timothy and Druckmann, Shaul and Li, Nuo and Svoboda, Karel} } @article {68708, title = {Predictive and robust gene selection for spatial transcriptomics.}, journal = {Nature Communications}, volume = {14}, year = {2023}, month = {2023 Apr 12}, pages = {2091}, abstract = {

A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell{\textquoteright}s molecular state. This typically requires targeting an a priori selection of genes, often covering less than 1\% of the genome, and a key question is how to optimally determine the small gene panel. We address this challenge by introducing a flexible deep learning framework, PERSIST, to identify informative gene targets for spatial transcriptomics studies by leveraging reference scRNA-seq data. Using datasets spanning different brain regions, species, and scRNA-seq technologies, we show that PERSIST reliably identifies panels that provide more accurate prediction of the genome-wide expression profile, thereby capturing more information with fewer genes. PERSIST can be adapted to specific biological goals, and we demonstrate that PERSIST{\textquoteright}s binarization of gene expression levels enables models trained on scRNA-seq data to generalize with to spatial transcriptomics data, despite the complex shift between these technologies.

}, keywords = {Gene Expression Profiling, Sequence Analysis, RNA, Single-Cell Analysis, Transcriptome}, issn = {2041-1723}, doi = {10.1038/s41467-023-37392-1}, author = {Covert, Ian and Gala, Rohan and Wang, Tim and Svoboda, Karel and S{\"u}mb{\"u}l, Uygar and Lee, Su-In} } @article {65897, title = {Cellpose: a generalist algorithm for cellular segmentation.}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Jan 07}, pages = {100-106}, abstract = {

Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.

}, issn = {1548-7105}, doi = {10.1038/s41592-020-01018-x}, author = {Stringer, Carsen and Wang, Tim and Michaelos, Michalis and Pachitariu, Marius} } @article {67815, title = {EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization.}, journal = {Cell}, volume = {184}, year = {2021}, month = {2021 Dec 01}, pages = {6361}, abstract = {

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300\ μm) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine spatially and molecularly defined subregions. EASI-FISH also facilitates iterative reanalysis of scRNA-seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2021.11.024}, author = {Wang, Yuhan and Eddison, Mark and Fleishman, Greg and Weigert, Martin and Xu, Shengjin and Wang, Tim and Rokicki, Konrad and Goina, Cristian and Henry, Fredrick E and Lemire, Andrew L and Schmidt, Uwe and Yang, Hui and Svoboda, Karel and Myers, Eugene W and Saalfeld, Stephan and Korff, Wyatt and Sternson, Scott M and Tillberg, Paul W} } @article {65826, title = {Metabolic determinants of cellular fitness dependent on mitochondrial reactive oxygen species.}, journal = {Science Advances}, volume = {6}, year = {2020}, month = {2020 Nov 04}, abstract = {

Mitochondria-derived reactive oxygen species (mROS) are required for the survival, proliferation, and metastasis of cancer cells. The mechanism by which mitochondrial metabolism regulates mROS levels to support cancer cells is not fully understood. To address this, we conducted a metabolism-focused CRISPR-Cas9 genetic screen and uncovered that loss of genes encoding subunits of mitochondrial complex I was deleterious in the presence of the mitochondria-targeted antioxidant mito-vitamin E (MVE). Genetic or pharmacologic inhibition of mitochondrial complex I in combination with the mitochondria-targeted antioxidants, MVE or MitoTEMPO, induced a robust integrated stress response (ISR) and markedly diminished cell survival and proliferation in vitro. This was not observed following inhibition of mitochondrial complex III. Administration of MitoTEMPO in combination with the mitochondrial complex I inhibitor phenformin decreased the leukemic burden in a mouse model of T cell acute lymphoblastic leukemia. Thus, mitochondrial complex I is a dominant metabolic determinant of mROS-dependent cellular fitness.

}, issn = {2375-2548}, doi = {10.1126/sciadv.abb7272}, author = {Kong, Hyewon and Reczek, Colleen R and McElroy, Gregory S and Steinert, Elizabeth M and Wang, Tim and Sabatini, David M and Chandel, Navdeep S} } @article {65058, title = {CHP1 regulates compartmentalized glycerolipid synthesis by activating GPAT4.}, journal = {Molecular Cell}, volume = {74}, year = {2019}, month = {2019 Apr 4}, pages = {45-58.e7}, abstract = {

Cells require a constant supply of fatty acids to survive and proliferate. Fatty acids incorporate into membrane and storage glycerolipids through a series of endoplasmic reticulum (ER) enzymes, but how these enzymes are regulated is not well understood. Here, using a combination of CRISPR-based genetic screens and unbiased lipidomics, we identified calcineurin B homologous protein 1 (CHP1) as a major regulator of ER glycerolipid synthesis. Loss of\ CHP1 severely reduces fatty acid incorporation and storage in mammalian cells and invertebrates. Mechanistically, CHP1 binds and activates GPAT4, which catalyzes the initial rate-limiting step in glycerolipid synthesis. GPAT4 activity requires CHP1 to be N-myristoylated, forming a key molecular interface between the two proteins. Interestingly, upon CHP1 loss, the peroxisomal enzyme, GNPAT, partially compensates for the loss of ER lipid synthesis, enabling cell proliferation. Thus, our work identifies a conserved regulator of glycerolipid metabolism and reveals plasticity in lipid synthesis of proliferating cells.

}, keywords = {3T3 Cells, Acyltransferases, Animals, Caenorhabditis elegans, Caenorhabditis elegans Proteins, Calcium-Binding Proteins, Cell Proliferation, Drosophila melanogaster, Drosophila Proteins, Endoplasmic Reticulum, Enzyme Activation, Gene Expression Regulation, Enzymologic, Glycerides, Glycerol-3-Phosphate O-Acyltransferase, HEK293 Cells, Hela Cells, Hep G2 Cells, Humans, Jurkat Cells, Lipogenesis, Mice, Palmitic Acid, Protein Binding}, issn = {1097-4164}, doi = {10.1016/j.molcel.2019.01.037}, author = {Zhu, Xiphias Ge and Nicholson Puthenveedu, Shirony and Shen, Yihui and La, Konnor and Ozlu, Can and Wang, Tim and Klompstra, Diana and Gultekin, Yetis and Chi, Jingyi and Fidelin, Justine and Peng, Tao and Molina, Henrik and Hang, Howard C and Min, Wei and Birsoy, K{\i}van{\c c}} } @article {49522, title = {Paring down to the essentials.}, journal = {Science (New York, N.Y.)}, volume = {362}, year = {2018}, month = {2018 Nov 23}, pages = {904}, issn = {1095-9203}, doi = {10.1126/science.aav6872}, author = {Wang, Tim} }