Morphological profiling by high-throughput single-cell biophysical fractometry

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2022, Номер unknown

Опубликована: Май 24, 2022

Abstract Complex and irregular cell architecture is known to statistically exhibit fractal geometry, i.e., a pattern resembles smaller part of itself. Although variations in cells are proven be closely associated with the disease-related phenotypes that otherwise obscured standard cell-based assays, analysis single-cell precision remains largely unexplored. To close this gap, here we develop an image-based approach quantifies multitude biophysical fractal-related properties at subcellular resolution. Taking together its high-throughput imaging performance (~10,000 cells/sec), technique, termed fractometry, offers sufficient statistical power for delineating cellular heterogeneity, context classification lung-cancer subtypes tracking cell-cycle progression. Further correlative shows fractometry can enrich morphological profiling depth spearhead systematic how morphology encodes health pathological conditions.

Язык: Английский

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VIA: Generalized and scalable trajectory inference in single-cell omics data

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2021, Номер unknown

Опубликована: Фев. 11, 2021

Abstract Inferring cellular trajectories using a variety of omic data is critical task in single-cell science. However, accurate prediction cell fates, and thereby biologically meaningful discovery, challenged by the sheer size data, diversity types, complexity their topologies. We present VIA, scalable trajectory inference algorithm that overcomes these limitations lazy-teleporting random walks to accurately reconstruct complex beyond tree-like pathways (e.g. cyclic or disconnected structures). show VIA robustly efficiently unravels fine-grained sub-trajectories 1.3-million-cell transcriptomic mouse atlas without losing global connectivity at such high count. further apply discovering elusive lineages less populous fates missed other methods across including proteomic, epigenomic, multi-omics datasets, new in-house morphological dataset.

Язык: Английский

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Image restoration of FACED microscopy by generative adversarial network

Опубликована: Март 16, 2023

We report the use of conditional generative adversarial network (cGAN) for restoring undersampled images captured in free-space angular-chirp-enhanced delay (FACED) microscopy. show that this deep-learning approach allows wider imaging field view (FOV) along FACED axis, without substantially sacrificing resolution, photon-budget and speed even with lower density scanning foci. This study could potential further extending applicability to a range biological applications require extended FOV imaging.

Язык: Английский

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High throughput QPM for Sickle Cell Disease Detection

Опубликована: Янв. 1, 2023

We propose to use holographic cytometry evaluate sickle cell disease patient samples and develop artificial intelligence that can screen for sickling phenotypes.

Язык: Английский

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Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2023, Номер unknown

Опубликована: Ноя. 6, 2023

Abstract Image-based cytometry faces constant challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge control samples across batches, have proved limited, especially with complex data. To overcome this, we introduce Cyto-Morphology Adversarial Distillation (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information batch variations, enabling integrated analysis multiple without assumptions manual annotation. Unique CytoMAD is its “morphology distillation”, symbiotically paired deep-learning image-contrast translation - offering additional interpretable insights into the label-free profiles. We demonstrate versatile efficacy augmenting power biophysical imaging cytometry. It allows classification human lung cancer types accurately recapitulates their progressive drug responses, even when trained concentration information. also applied jointly analyze tumor biopsies non-small-cell patients’ reveal previously unexplored heterogeneity, linked epithelial-mesenchymal plasticity, standard fluorescence markers overlook. holds promises substantiate wide adoption for cost-effective diagnostic screening applications.

Язык: Английский

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Light-sheet dual-modality imaging flow cytometry with a single detector for label-free particle analysis

Опубликована: Ноя. 24, 2023

There is a growing interest in the development of imaging flow cytometry techniques that can simultaneously capture dual-modality images single cells on detector. In this study, we developed label-free light-sheet dualmodality cytometer capable capturing bright-field and two-dimensional (2D) light scattering individual particles The system uses principle hydrodynamic focusing to make microspheres file. laser metal halide lamp beams are combined as sources, which directed onto microspheres, providing 2D patterns particles. two optical channels collect collected by CMOS By employing cytometry, demonstrated obtaining analysis light-scattering micrometer-sized promising for applications single-cell clinical analysis.

Язык: Английский

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