Towards ‘end-to-end’ analysis and understanding of biological timecourse data

Abstract

Petabytes of increasingly complex and multidimensional live cell and tissue imaging data are generated every year. These videos hold large promise for understanding biology at a deep and fundamental level, as they capture single-cell and multicellular events occurring over time and space. However, the current modalities for analysis and mining of these data are scattered and user-specific, preventing more unified analyses from being performed over different datasets and obscuring possible scientific insights. Here, we propose a unified pipeline for storage, segmentation, analysis, and statistical parametrization of live cell imaging datasets.

Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion

“…We combined live-cell imaging and machine learning to infer the differentiation state of single cells during the process of muscle precursor cell differentiation. Many studies highlight the rich information encapsulated in single-cell dynamics that, with the aid of supervised or unsupervised machine learning, enable effective identification of sub-populations and discrimination of perturbations (Choi et al, 2021 ; Goglia et al, 2020 ; Jacques et al, 2021 ; Jena et al, 2022 ; Kimmel et al, 2018 ; Valls and Esposito, 2022 ), that cannot be inferred from static snapshot images (Copperman et al, 2021 ; Wang et al, 2020 ; Wu et al, 2022 ). For example, approaches that rely on static snapshots make it extremely hard to infer trajectories that deviate from the mainstream cell-state progression because they are confounded by cell-to-cell variability.…”

Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion

“…We combined live cell imaging and machine learning to infer the differentiation state of single cells during the process of muscle precursor cell differentiation. Many studies highlight the rich information encapsulated in single-cell dynamics that, with the aid of supervised or unsupervised machine learning, enable effective identification of sub-populations and discrimination of perturbations (Choi et al, 2021; Goglia et al, 2020; Jacques et al, 2021; Jena et al, 2022; Kimmel et al, 2018; Valls & Esposito, 2022), that cannot be inferred from static snapshot images (Copperman et al, 2021; Wang et al, 2020; Wu et al, 2022). For example, approaches that rely on static snapshots make it extremely hard to infer trajectories that deviate from the mainstream cell state progression because they are confounded by cell-to-cell variability.…”