Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging

Abstract

Mesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to reproduce, largely due to the inherently significant heterogeneity in MSCs, which has not been well investigated. To quantify cell heterogeneity, a standard approach is to measure marker expression on the protein level via immunochemistry assays. Performing such measurements non-invasively and at scale has remained challenging as conventional methods such as flow cytometry and immunofluorescence microscopy typically require cell fixation and laborious sample preparation. Here, we developed an artificial intelligence (AI)-based method that converts transmitted light microscopy images of MSCs into quantitative measurements of protein expression levels. By training a U-Net+ conditional generative adversarial network (cGAN) model that accurately (mean $$r_s$$ r s = 0.77) predicts expression of 8 MSC-specific markers, we showed that expression of surface markers provides a heterogeneity characterization that is complementary to conventional cell-level morphological analyses. Using this label-free imaging method, we also observed a multi-marker temporal-spatial fluctuation of protein distributions in live MSCs. These demonstrations suggest that our AI-based microscopy can be utilized to perform quantitative, non-invasive, single-cell, and multi-marker characterizations of heterogeneous live MSC culture. Our method provides a foundational step toward the instant integrative assessment of MSC properties, which is critical for high-throughput screening and quality control in cellular therapies.

Label-free prediction of cell painting from brightfield images

“…While brightfield imaging overcomes many drawbacks of fluorescent labelling, it lacks the specificity and clear separation of the structures of interest. Deep learning methods have shown promise in augmenting the information available in brightfield when trained with fluorescent signals as a domain transfer problem 9 .…”

Unleashing the power of high-throughput bright-field imaging for enhanced mesenchymal cell separation: a novel supervised clustering approach in vitro augmentation of healthy and stressful conditions

“…Yet, manufacturing these therapeutic agents introduces unique challenges, including issues related to donor variability, tissue source, and differences in the media environment [5,6]. To address these constraints, the use of high-throughput imaging and artificial intelligence (AI) technologies has been recently advanced, offering speedy and in-depth insights to enhance bioprocess analytics in CT manufacturing [7][8][9].…”

“…Examples include the work of Zhang et al, who employed deep learning for label-free nuclei detection from MSC implicit phase information [7], and Kim et al conducted a high-throughput screening of MSC lines using deep learning techniques [8]. Further, Imboden et al, who used AI-driven label-free imaging to examine MSC heterogeneities [9]. While these investigations underscore the growing interest in AI and imaging technologies, the AI models' interpretation has generally been confined to global feature analysis, thereby overlooking the relative importance of individual prediction features.…”

Subcellular spatially resolved gene neighborhood networks in single cells

“…FISH-based multiplexing methods localize single RNA transcripts, enabling the study of the spatial organization of RNAenriched compartments in subcellular volumes, a feature previously limited to live-cell imaging. 30 For instance, -Actin molecules are observed to be localized at the cell edge and focal adhesion points, allowing cells to efficiently migrate and polarize. 31 In cells, complex regulatory relationships between genes maintain gene-expression phenotypes.…”