Biology subjects

Sapre, A.

Publications and source records attributed to Sapre, A..

5 recordsLinked to original sources

bioRxiv2025

AI-driven analysis for real-time detection of unstained microscopic cell culture images

AI-based image recognition has significantly advanced the analysis of tissues and individual cells both in the context of translational studies and diagnostics. To date, recognition is primarily based on the identification of certain cell characteristics (e.g. by staining). The morphological assessment of unstained cells holds additional potential, as it allows for virtually real-time assessment without the need to manipulate the cells. This facilitates longitudinal observations, as required for drug testing, and forms a basis for autonomous experimental execution. A semi-automated cell culture system (AICE3, LabMaite) was used to culture myeloid leukemic cell lines (K562, HL-60, Kasumi-1). K562 cells were treated with hemin and PMA to induce erythroid and megakaryocytic differentiation, respectively. Cell images were acquired using automated bright field microscopy. Images were used to train an AI model using an NVIDIA DGX A100 GPU with Ultralytics YOLOv8. Morphologic features were extracted using RedTell. The model reliably distinguished K562 cells from HL-60 and Kasumi-1 using >400 images per class (average >15 cells/image). Bounding boxes were generated correctly (mAP@.5 >98%); precision and sensitivity exceeded 97%. Validation on an external K562 dataset confirmed these results. Classification of all three cell lines achieved >97% sensitivity/specificity and 94.6% precision. To test drug response, we used YOLOv8-s to distinguish untreated K562 cells from those undergoing erythroid or megakaryocytic differentiation (n >3,000 annotations). Precision, sensitivity, and specificity were >95%. RedTell identified 3 of 74 morphological traits contributing significantly to class separation. We demonstrate accurate, near real-time detection of unstained cells, enabling future AI-based drug testing.

cell biology↗

bioRxiv2025

Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling

Building predictive models of the cell requires systematically mapping how perturbations reshape each cells state, function, and behavior. Here, we present Tahoe-100M, a giga-scale single-cell atlas of 100 million transcriptomic profiles measuring how each of 1,100 small-molecule perturbations impact cells across 50 cancer cell lines. Our high-throughput Mosaic platform, composed of a highly diverse and optimally balanced "cell village", reduces batch effects and enables parallel profiling of thousands of conditions at single-cell resolution at an unprecedented scale. As the largest single-cell dataset to date, Tahoe-100M enables artificial-intelligence (AI)-driven models to learn context-dependent functions, capturing fundamental principles of gene regulation and network dynamics. Although we leverage cancer models and pharmacological compounds to create this resource, Tahoe-100M is fundamentally designed as a broadly applicable perturbation atlas and supports deeper insights into cell biology across multiple tissues and contexts. By publicly releasing this atlas, we aim to accelerate the creation and development of robust AI frameworks for systems biology, ultimately improving our ability to predict and manipulate cellular behaviors across a wide range of applications.

genomics↗

bioRxiv2022

High sensitivity single cell RNA sequencing with split pool barcoding

Single cell RNA sequencing (scRNA-seq) has become a core tool for researchers to understand biology. As scRNA-seq has become more ubiquitous, many applications demand higher scalability and sensitivity. Split-pool combinatorial barcoding makes it possible to scale projects to hundreds of samples and millions of cells, overcoming limitations of previous droplet based technologies. However, there is still a need for increased sensitivity for both droplet and combinatorial barcoding based scRNA-seq technologies. To meet this need, here we introduce an updated combinatorial barcoding method for scRNA-seq with dramatically improved sensitivity. To assess performance, we profile a variety of sample types, including cell lines, human peripheral blood mononuclear cells (PBMCs), mouse brain nuclei, and mouse liver nuclei. When compared to the previously best performing approach, we find up to a 2.6-fold increase in unique transcripts detected per cell and up to a 1.8-fold increase in genes detected per cell. These improvements to transcript and gene detection increase the resolution of the resulting data, making it easier to distinguish cell types and states in heterogeneous samples. Split-pool combinatorial barcoding already enables scaling to millions of cells, the ability to perform scRNA-seq on previously fixed and frozen samples, and access to scRNA-seq without the need to purchase specialized lab equipment. Our hope is that by combining these previous advantages with the dramatic improvements to sensitivity presented here, we will elevate the standards and capabilities of scRNA-seq for the broader community.

genomics↗

bioRxiv2021

Relevance of Circulating Hybrid Cells as a Non-Invasive Biomarker for Myriad Solid Tumors

AbstractMetastatic progression defines the final stages of tumor evolution and underlies the majority of cancer-related deaths. The heterogeneity in disseminated tumor cell populations capable of seeding and growing in distant organ sites contributes to the development of treatment resistant disease. We recently reported the identification of a novel tumor-derived cell population, circulating hybrid cells (CHCs), harboring attributes from both macrophages and neoplastic cells, including functional characteristics important to metastatic spread. These disseminated hybrids outnumber conventionally defined circulating tumor cells (CTCs) in cancer patients. It is unknown if CHCs represent a generalized cancer mechanism for cell dissemination, or if this population is relevant to the metastatic cascade. Herein, we detect CHCs in the peripheral blood of patients with cancer in myriad disease sites encompassing epithelial and non-epithelial malignancies. Further, we demonstrate that in vivo-derived hybrid cells harbor tumor-initiating capacity in murine cancer models and that CHCs from human breast cancer patients express stem cell antigens, features consistent with the ability to seed and grow at metastatic sites. Finally, we reveal heterogeneity of CHC phenotypes reflect key tumor features, including oncogenic mutations and functional protein expression. Importantly, this novel population of disseminated neoplastic cells opens a new area in cancer biology and renewed opportunity for battling metastatic disease. Simple SummaryThere is an incomplete understanding of circulating neoplastic cell populations and the fundamental mechanisms that drive dissemination, immune evasion, and growth --all critical information to more effectively prevent and treat cancer progression. A novel disseminated tumor cell population, circulating hybrid cells, are detected across many cancer types and carry functional tumor-initiating properties. Additionally, circulating hybrid cells are found at significantly higher levels than conventionally defined circulating tumor cells. Our study demonstrates that neoplastic hybrid cells harbor phenotypic and genetic characteristics of tumor and immune cells, display stem features, and are a generalizable phenomenon in solid tumors. Circulating hybrid cells therefore have relevance as a novel biomarker and open a new field of study in malignancy.

cancer biology↗

bioRxiv2020

Dynamic Laser Speckle Imaging meets Machine Learning to enable Rapid Antibacterial Susceptibility Testing (DyRAST)

Rapid antibacterial susceptibility testing (RAST) methods which measure change of a bacterial phenotype in response to a given treatment are of significant importance in healthcare, as they can assist care-givers in timely administration of the right treatment. Various RAST techniques have been reported for tracking bacterial phenotypes, such as size, shape, motion, and metabolic activity. However, they still require bulky and expensive instruments (which hinders their application in resource-limited environments) and/or utilize labeling reagents (which can interfere with antibiotics and add to cost). Furthermore, the existing ultra-rapid methods do not address possible adaptation of gradual adaptation of bacteria to antibiotics, which can lead to false interpretation of resistance when using ultra-rapid methods. In this work, we present a RAST approach leveraging machine learning analysis of time-resolved dynamic laser speckle imaging (DLSI) results to accurately predict the minimum inhibitory concentration (MIC) of a model strain of Escherichia coli in 60 minutes, compared to 6 hours using the currently FDA-approved phenotype-based RAST technique. To demonstrate the DLSI performance, we studied the effect of a {beta}-lactam ampicillin and an aminoglycoside gentamicin on Escherichia coli strain K-12. DLSI captures change of bacterial motion/division in response to treatment. The machine learning algorithm was trained and validated using the overnight results of gold standard, broth microdilution method. Empowered by machine learning, DyRAST can predict MIC with high accuracy comparable to gold standard methods through a voting strategy.

microbiology↗