News

• 11 2025

  Nature Methods published our collaborative work on using ESPRESSO to correlate phenotype changes with gene expression spearheaded by the Digman and Scipioni Labs Scott Atwood

  Omics technologies such as genomics, transcriptomics, proteomics and metabolomics methods, have been instrumental in improving our understanding of complex biological systems by providing high-dimensional phenotypes of cell populations and single cells. Despite fast-paced advancements, these methods are limited in their ability to include a temporal dimension. Here, we introduce ESPRESSO (Environmental Sensor Phenotyping RElayed by Subcellular Structures and Organelles), a technique that provides single-cell, high-dimensional phenotyping resolved in space and time. ESPRESSO combines fluorescent labeling, advanced microscopy and image and data analysis methods to extract morphological and functional information from organelles at the single-cell level. We validate ESPRESSO’s methodology and its application across numerous cellular systems for the analysis of cell type, stress response, differentiation and immune cell polarization. We show that ESPRESSO can correlate phenotype changes with gene expression, and demonstrate its applicability to 3D cultures, offering a path to improved spatially and temporally resolved biological exploration of cellular states Read More PDF

• 10 2025

  BMC Bioinformatics published our collaborative work on using BigSur for statistically principled feature selection for single cell transcriptomics Scott Atwood

  The high dimensionality of data in single cell transcriptomics (scRNAseq) requires investigators to choose subsets of genes ("feature selection") for downstream analysis (e.g., unsupervised cell clustering). The evaluation of different approaches to feature selection is hampered by the fact that, as we show here, the difficulty of feature selection can vary greatly, depending on the dataset being analyzed. For routine cell type identification, even randomly chosen features can perform well, but for cell type differences that are subtle, both number of features and selection strategy matter strongly. We present a simple feature selection method grounded in an analytical model that allows for interpretable delineation of how many and which features to choose, facilitating identification of biologically meaningful rare cell types. We compare this method to default methods in scanpy and Seurat, as well as SCTransform, showing how greater accuracy can often be achieved with surprisingly few, well-chosen features. Feature selection is a critical step in scRNAseq for downstream analyses. We explore the pitfalls that can arise from incautious feature selection and present a statistical method to facilitate improved outcomes. Read More PDF

• 5 2025

  Cell Death and Disease published our collaborative work on SFRP1 as an inhibitor of epidermal progenitor proliferation spearheaded by the Sun labScott Atwood

  Secreted proteins are crucial for the structure and functions of the human epidermis, but the full repertoire of the keratinocyte secretome has not been experimentally defined. In this study, we performed mass spectrometry on conditioned media from primary human keratinocytes, identifying 406 proteins with diverse roles in adhesion, migration, proliferation, proteolysis, signal transduction, and innate immunity. To leverage this new dataset, we developed a novel colony formation assay-based CRISPR screen to investigate the functions of uncharacterized secreted proteins on epidermal stem cells. The screen identified six candidate proteins that promoted proliferation of epidermal progenitors and two proteins that inhibited it. Secreted frizzled-related protein-1 (SFRP1) was the most potent inhibitor. We discovered that SFRP1 restrained clonogenic keratinocyte proliferation by inhibiting Wnt signaling as well as blocking ectopic expression of leukemia inhibitory factor (LIF). Collectively, our study expands our knowledge of the keratinocyte secretome, establishes a novel CRISPR screen to assess the function of non-cell autonomous factors, and highlights SFRP1's role in regulating epidermal balance. Read More PDF

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