AIMC Topic: Single-Cell Analysis

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Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies.

PLoS computational biology
Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static...

Morphological features of single cells enable accurate automated classification of cancer from non-cancer cell lines.

Scientific reports
Accurate cancer detection and diagnosis is of utmost importance for reliable drug-response prediction. Successful cancer characterization relies on both genetic analysis and histological scans from tumor biopsies. It is known that the cytoskeleton is...

Training a deep learning model for single-cell segmentation without manual annotation.

Scientific reports
Advances in the artificial neural network have made machine learning techniques increasingly more important in image analysis tasks. Recently, convolutional neural networks (CNN) have been applied to the problem of cell segmentation from microscopy i...

CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq data.

PLoS computational biology
Cis-Regulatory elements (cis-REs) include promoters, enhancers, and insulators that regulate gene expression programs via binding of transcription factors. ATAC-seq technology effectively identifies active cis-REs in a given cell type (including from...

EnTSSR: A Weighted Ensemble Learning Method to Impute Single-Cell RNA Sequencing Data.

IEEE/ACM transactions on computational biology and bioinformatics
The advancements of single-cell RNA sequencing (scRNA-seq) technologies have provided us unprecedented opportunities to characterize cellular states and investigate the mechanisms of complex diseases. Due to technical issues such as dropout events, s...

Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning.

Nature cell biology
Simultaneous imaging of various facets of intact biological systems across multiple spatiotemporal scales is a long-standing goal in biology and medicine, for which progress is hindered by limits of conventional imaging modalities. Here we propose us...

Robotic high-throughput biomanufacturing and functional differentiation of human pluripotent stem cells.

Stem cell reports
Efficient translation of human induced pluripotent stem cells (hiPSCs) requires scalable cell manufacturing strategies for optimal self-renewal and functional differentiation. Traditional manual cell culture is variable and labor intensive, posing ch...

Multiview confocal super-resolution microscopy.

Nature
Confocal microscopy remains a major workhorse in biomedical optical microscopy owing to its reliability and flexibility in imaging various samples, but suffers from substantial point spread function anisotropy, diffraction-limited resolution, depth-d...

Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis.

International journal of molecular sciences
BACKGROUND: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell.

Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics.

Communications biology
Recent single-cell multimodal data reveal multi-scale characteristics of single cells, such as transcriptomics, morphology, and electrophysiology. However, integrating and analyzing such multimodal data to deeper understand functional genomics and ge...