Rapid single-cell physical phenotyping of mechanically dissociated tissue biopsies

Automated spatial omics landscape analysis approach reveals novel tissue architectures in ulcerative colitis

The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.

Detecting normal and cancer skin cells via glycosylation and adhesion signatures: A path to enhanced microfluidic phenotyping

Recruiting circulating cells based on interactions between surface receptors and corresponding ligands holds promise for capturing cells with specific adhesive properties. Our study investigates the adhesion of skin cells to specific lectins, particularly focusing on advancements in lectin-based biosensors with diagnostic potential. We explore whether we can successfully capture normal skin (melanocytes and keratinocytes) and melanoma (WM35, WM115, WM266-4) cells in a low-shear flow environment by coating surfaces with lectins. Specifically, we coated surfaces with Dolichos biflorus (DBA) and Maackia Amurensis (MAL) lectins, which were used to detect and capture specific skin cells from the flow of cell mixture. Alterations in glycan expression (confirmed by fluorescent microscopy) demonstrated that DBA binds predominantly to normal skin cells, while MAL interacts strongly with melanoma cells. Assessing adhesion under static and dynamic low-shear stress conditions (up to 30 mPa) underscores the reliability of DBA and MAL as markers for discriminating specific cell type. Melanocytes and keratinocytes adhere to DBA-coated surfaces, while melanoma cells prefer MAL-coated surfaces. A comprehensive analysis encompassing cell shape, cytoskeleton, and focal adhesions shows the independence of our approach from the inherent characteristics of cells, thus demonstrating its robustness. Our results carry practical implications for lectin-biosensor designs, emphasizing the significance of glycan-based discrimination of pathologically altered cells. Combined with microfluidics, it demonstrates the value of cell adhesion as a discriminant of cancer-related changes, with potential applications spanning diagnostics, therapeutic interventions, and advanced biomedical technologies.

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

Image-based cytometry faces 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 or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its "morphology distillation", symbiotically paired with deep-learning image-contrast translation-offering additional interpretable insights into label-free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD  also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost-effective diagnosis and screening.

Liquid Biopsy Instrument for Ultra-Fast and Label-Free Detection of Circulating Tumor Cells

Rapid diagnosis and real-time monitoring are of great important in the fight against cancer. However, most available diagnostic technologies are time-consuming and labor-intensive and are commonly invasive. Here, we describe CytoExam, an automatic liquid biopsy instrument designed based on inertial microfluidics and impedance cytometry, which uses a deep learning algorithm for the analysis of circulating tumor cells (CTCs). In silico and in vitro experiments demonstrated that CytoExam could achieve label-free detection of CTCs in the peripheral blood of cancer patients within 15 min. The clinical applicability of CytoExam was also verified using peripheral blood samples from 10 healthy donors and >50 patients with breast, colorectal, or lung cancer. Significant differences in the number of collected cells and predicted CTCs were observed between the 2 groups, with variations in the dielectric properties of the collected cells from cancer patients also being observed. The ultra-fast and minimally invasive features of CytoExam may pave the way for new paths for cancer diagnosis and scientific research.

Single-Cell Mechanics: Structural Determinants and Functional Relevance

The mechanical phenotype of a cell determines its ability to deform under force and is therefore relevant to cellular functions that require changes in cell shape, such as migration or circulation through the microvasculature. On the practical level, the mechanical phenotype can be used as a global readout of the cell's functional state, a marker for disease diagnostics, or an input for tissue modeling. We focus our review on the current knowledge of structural components that contribute to the determination of the cellular mechanical properties and highlight the physiological processes in which the mechanical phenotype of the cells is of critical relevance. The ongoing efforts to understand how to efficiently measure and control the mechanical properties of cells will define the progress in the field and drive mechanical phenotyping toward clinical applications.

Multiparameter Mechanical Phenotyping for Accurate Cell Identification Using High-Throughput Microfluidic Deformability Cytometry

Mechanical phenotyping has been widely employed for single-cell analysis over recent years. However, most previous works on characterizing the cellular mechanical properties measured only a single parameter from one image. In this paper, the quasi-real-time multiparameter analysis of cell mechanical properties was realized using high-throughput adjustable deformability cytometry. We first extracted 12 deformability parameters from the cell contours. Then, the machine learning for cell identification was performed to preliminarily verify the rationality of multiparameter mechanical phenotyping. The experiments on characterizing cells after cytoskeletal modification verified that multiple parameters extracted from the cell contours contributed to an identification accuracy of over 80%. Through continuous frame analysis of the cell deformation process, we found that temporal variation and an average level of parameters were correlated with cell type. To achieve quasi-real-time and high-precision multiplex-type cell detection, we constructed a back propagation (BP) neural network model to complete the fast identification of four cell lines. The multiparameter detection method based on time series achieved cell detection with an accuracy of over 90%. To solve the challenges of cell rarity and data lacking for clinical samples, based on the developed BP neural network model, the transfer learning method was used for the identification of three different clinical samples, and finally, a high identification accuracy of approximately 95% was achieved.

A novel approach to generate enzyme-free single cell suspensions from archived tissues for miRNA sequencing

Obtaining high-quality omics data at the single-cell level from archived human tissue samples is crucial for gaining insights into cellular heterogeneity and pushing the field of personalized medicine forward. In this technical brief we present a comprehensive methodological framework for the efficient enzyme-free preparation of tissue-derived single cell suspensions and their conversion into single-cell miRNA sequencing libraries. The resulting data from this study have the potential to deepen our understanding of miRNA expression at the single-cell level and its relevance in the context of the examined tissues. The workflow encompasses tissue collection, RNALater immersion, storage, thawing, TissueGrinder-mediated dissociation, miRNA lysis, library preparation, sequencing, and data analysis. Quality control measures ensure reliable miRNA data, with specific attention to sample quality. The UMAP analysis reveals tissue-specific cell clustering, while miRNA diversity reflects tissue variations. The presented workflow effectively processes preserved tissues, extending opportunities for retrospective analysis and biobank utilization.

High-throughput mechanical phenotyping and transcriptomics of single cells

The molecular system regulating cellular mechanical properties remains unexplored at single-cell resolution mainly due to a limited ability to combine mechanophenotyping with unbiased transcriptional screening. Here, we describe an electroporation-based lipid-bilayer assay for cell surface tension and transcriptomics (ELASTomics), a method in which oligonucleotide-labelled macromolecules are imported into cells via nanopore electroporation to assess the mechanical state of the cell surface and are enumerated by sequencing. ELASTomics can be readily integrated with existing single-cell sequencing approaches and enables the joint study of cell surface mechanics and underlying transcriptional regulation at an unprecedented resolution. We validate ELASTomics via analysis of cancer cell lines from various malignancies and show that the method can accurately identify cell types and assess cell surface tension. ELASTomics enables exploration of the relationships between cell surface tension, surface proteins, and transcripts along cell lineages differentiating from the haematopoietic progenitor cells of mice. We study the surface mechanics of cellular senescence and demonstrate that RRAD regulates cell surface tension in senescent TIG-1 cells. ELASTomics provides a unique opportunity to profile the mechanical and molecular phenotypes of single cells and can dissect the interplay among these in a range of biological contexts.

Automated Spatial Omics Landscape Analysis Approach Reveals Novel Tissue Architectures in Ulcerative Colitis

The utility of spatial omics in leveraging cellular interactions in normal and diseased states for precision medicine is hampered by a lack of strategies for matching disease states with spatial heterogeneity-guided cellular annotations. Here we use a spatial context-dependent approach that matches spatial pattern detection to cell annotation. Using this approach in existing datasets from ulcerative colitis patient colonic biopsies, we identified architectural complexities and associated difficult-to-detect rare cell types in ulcerative colitis germinal-center B cell follicles. Our approach deepens our understanding of health and disease pathogenesis, illustrates a strategy for automating nested architecture detection for highly multiplexed spatial biology data, and informs precision diagnosis and therapeutic strategies.

Evidence and therapeutic implications of biomechanically regulated immunosurveillance in cancer and other diseases

Disease progression is usually accompanied by changes in the biochemical composition of cells and tissues and their biophysical properties. For instance, hallmarks of cancer include the stiffening of tissues caused by extracellular matrix remodelling and the softening of individual cancer cells. In this context, accumulating evidence has shown that immune cells sense and respond to mechanical signals from the environment. However, the mechanisms regulating these mechanical aspects of immune surveillance remain partially understood. The growing appreciation for the 'mechano-immunology' field has urged researchers to investigate how immune cells sense and respond to mechanical cues in various disease settings, paving the way for the development of novel engineering strategies that aim at mechanically modulating and potentiating immune cells for enhanced immunotherapies. Recent pioneer developments in this direction have laid the foundations for leveraging 'mechanical immunoengineering' strategies to treat various diseases. This Review first outlines the mechanical changes occurring during pathological progression in several diseases, including cancer, fibrosis and infection. We next highlight the mechanosensitive nature of immune cells and how mechanical forces govern the immune responses in different diseases. Finally, we discuss how targeting the biomechanical features of the disease milieu and immune cells is a promising strategy for manipulating therapeutic outcomes.

Rebuilding the microenvironment of primary tumors in humans: a focus on stroma

Conventional tumor models have critical shortcomings in that they lack the complexity of the human stroma. The heterogeneous stroma is a central compartment of the tumor microenvironment (TME) that must be addressed in cancer research and precision medicine. To fully model the human tumor stroma, the deconstruction and reconstruction of tumor tissues have been suggested as new approaches for in vitro tumor modeling. In this review, we summarize the heterogeneity of tumor-associated stromal cells and general deconstruction approaches used to isolate patient-specific stromal cells from tumor tissue; we also address the effect of the deconstruction procedure on the characteristics of primary cells. Finally, perspectives on the future of reconstructed tumor models are discussed, with an emphasis on the essential prerequisites for developing authentic humanized tumor models.

Evaluation of Collagenase Activity from Crab Hepatopancreas in Different Model Systems

The effect of Kamchatka crab hepatopancreas containing three collagenolytic isoenzymes Collagenase KK and proteinases of Streptomyces lavendulae on metabolic activity and cell death were carried out on in vitro models. It was shown that changes in the protein structure under the influence of Collagenase KK occurred earlier than under the effect of bacterial proteinases. At the same time, activity of Collagenase KK was significantly higher than that of bacterial proteinases (p<0.01). Both preparations had a pronounced time- and dose-dependent effects on metabolic activity of cells. Collagenase KK had low cytotoxic effect, and cells mainly died by apoptosis. Thus, hepatopancreas collagenase has a high activity and proapoptotic effect on cells and can be used in low concentrations for enzymatic disaggregation of tissues.

High-throughput single-cell, single-mitochondrial DNA assay using hydrogel droplet microfluidics

There is growing interest in understanding the biological implications of single cell heterogeneity and intracellular heteroplasmy of mtDNA, but current methodologies for single-cell mtDNA analysis limit the scale of analysis to small cell populations. Although droplet microfluidics have increased the throughput of single-cell genomic, RNA, and protein analysis, their application to sub-cellular organelle analysis has remained a largely unsolved challenge. Here, we introduce an agarose-based droplet microfluidic approach for single-cell, single-mtDNA analysis, which allows simultaneous processing of hundreds of individual mtDNA molecules within >10,000 individual cells. Our microfluidic chip encapsulates individual cells in agarose beads, designed to have a sufficiently dense hydrogel network to retain mtDNA after lysis and provide a robust scaffold for subsequent multi-step processing and analysis. To mitigate the impact of the high viscosity of agarose required for mtDNA retention on the throughput of microfluidics, we developed a parallelized device, successfully achieving ~95% mtDNA retention from single cells within our microbeads at >700,000 drops/minute. To demonstrate utility, we analyzed specific regions of the single mtDNA using a multiplexed rolling circle amplification (RCA) assay. We demonstrated compatibility with both microscopy, for digital counting of individual RCA products, and flow cytometry for higher throughput analysis.

Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae

Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies.

Using Adhesive Micropatterns and AFM to Assess Cancer Cell Morphology and Mechanics

The mechanical properties of living cells reflect their physiological and pathological state. In particular, cancer cells undergo cytoskeletal modifications that typically make them softer than healthy cells, a property that could be used as a diagnostic tool. However, this is challenging because cells are complex structures displaying a broad range of morphologies when cultured in standard 2D culture dishes. Here, we use adhesive micropatterns to impose the cell geometry and thus standardize the mechanics and morphologies of cancer cells, which we measure by atomic force microscopy (AFM), mechanical nanomapping, and membrane nanotube pulling. We show that micropatterning cancer cells leads to distinct morphological and mechanical changes for different cell lines. Micropatterns did not systematically lower the variability in cell elastic modulus distribution. These effects emerge from a variable cell spreading rate associated with differences in the organization of the cytoskeleton, thus providing detailed insights into the structure-mechanics relationship of cancer cells cultured on micropatterns. Combining AFM with micropatterns reveals new mechanical and morphological observables applicable to cancer cells and possibly other cell types.

Revolutionizing microfluidics with artificial intelligence: a new dawn for lab-on-a-chip technologies

Keisuke Goda, Hang Lu, Peng Fei, and Jochen Guck introduce the AI in Microfluidics themed collection, on revolutionizing microfluidics with artificial intelligence: a new dawn for lab-on-a-chip technologies.

Pilot study on the influence of acute alcohol exposure on biophysical parameters of leukocytes

Objective: This pilot study explores the influence of acute alcohol exposure on cell mechanical properties of steady-state and activated leukocytes conducted with real-time deformability cytometry. Methods: Nineteen healthy male volunteers were enrolled to investigate the effect of binge drinking on biophysical properties and cell counts of peripheral blood leukocytes. Each participant consumed an individualized amount of alcohol to achieve a blood alcohol concentration of 1.2 ‰ as a mean peak. In addition, we also incubated whole blood samples from healthy donors with various ethanol concentrations and performed stimulation experiments using lipopolysaccharide and CytoStim™ in the presence of ethanol. Results: Our findings indicate that the biophysical properties of steady-state leukocytes are not significantly affected by a single episode of binge drinking within the first two hours. However, we observed significant alterations in relative cell counts and a shift toward a memory T cell phenotype. Moreover, exposure to ethanol during stimulation appears to inhibit the cytoskeleton reorganization of monocytes, as evidenced by a hindered increase in cell deformability. Conclusion: Our observations indicate the promising potential of cell mechanical analysis in understanding the influence of ethanol on immune cell functions. Nevertheless, additional investigations in this field are warranted to validate biophysical properties as biomarkers or prognostic indicators for alcohol-related changes in the immune system.