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1. Trellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses

   https://doi.org/10.1016/j.cell.2023.11.005  

   Ramos_Zapatero, Maria; Tong, Alexander; Opzoomer, James W; O’Sullivan, Rhianna; Cardoso_Rodriguez, Ferran; Sufi, Jahangir; Vlckova, Petra; Nattress, Callum; Qin, Xiao; Claus, Jeroen; et al (December 2023, Cell)

   Patient-derived organoids (PDOs) can model personalized therapy responses; however, current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly multiplexed mass cytometry platform to measure post- translational modification (PTM) signaling, DNA damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer-associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell da- tasets, we developed ‘‘Trellis’’—a highly scalable, tree-based treatment effect analysis method. Trellis single- cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rarer, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate PDO plasticity—shifting proliferative colonic stem cells (proCSCs) to slow-cycling revival colonic stem cells (revCSCs) to protect cancer cells from chemotherapy. 

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2. Altered physical phenotypes of leukemia cells that survive chemotherapy treatment

   https://doi.org/10.1093/intbio/zyad006  

   Ly, Chau; Ogana, Heather; Kim, Hye Na; Hurwitz, Samantha; Deeds, Eric J; Kim, Yong-Mi; Rowat, Amy C (April 2023, Integrative Biology)

   The recurrence of cancer following chemotherapy treatment is a major cause of death across solid and hematologic cancers. In B-cell acute lymphoblastic leukemia (B-ALL), relapse after initial chemotherapy treatment leads to poor patient outcomes. Here we test the hypothesis that chemotherapy-treated versus control B-ALL cells can be characterized based on cellular physical phenotypes. To quantify physical phenotypes of chemotherapy-treated leukemia cells, we use cells derived from B-ALL patients that are treated for 7 days with a standard multidrug chemotherapy regimen of vincristine, dexamethasone, and L-asparaginase (VDL). We conduct physical phenotyping of VDL-treated versus control cells by tracking the sequential deformations of single cells as they flow through a series of micron-scale constrictions in a microfluidic device; we call this method Quantitative Cyclical Deformability Cytometry. Using automated image analysis, we extract time-dependent features of deforming cells including cell size and transit time (TT) with single-cell resolution. Our findings show that VDL-treated B-ALL cells have faster TTs and transit velocity than control cells, indicating that VDL-treated cells are more deformable. We then test how effectively physical phenotypes can predict the presence of VDL-treated cells in mixed populations of VDL-treated and control cells using machine learning approaches. We find that TT measurements across a series of sequential constrictions can enhance the classification accuracy of VDL-treated cells in mixed populations using a variety of classifiers. Our findings suggest the predictive power of cell physical phenotyping as a complementary prognostic tool to detect the presence of cells that survive chemotherapy treatment. Ultimately such complementary physical phenotyping approaches could guide treatment strategies and therapeutic interventions. 

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3. Cell morphology-based machine learning models for human cell state classification

   Li, Y.; Nowak, C.; Pham, U.; Nguyen, K; Bleris, L. (May 2021, npj systems biology and applications)

   null (Ed.)

   Herein, we implement and access machine learning architectures to ascertain models that differentiate healthy from apoptotic cells using exclusively forward (FSC) and side (SSC) scatter flow cytometry information. To generate training data, colorectal cancer HCT116 cells were subjected to miR-34a treatment and then classified using a conventional Annexin V/propidium iodide (PI)-staining assay. The apoptotic cells were defined as Annexin V-positive cells, which include early and late apoptotic cells, necrotic cells, as well as other dying or dead cells. In addition to fluorescent signal, we collected cell size and granularity information from the FSC and SSC parameters. Both parameters are subdivided into area, height, and width, thus providing a total of six numerical features that informed and trained our models. A collection of logistical regression, random forest, k-nearest neighbor, multilayer perceptron, and support vector machine was trained and tested for classification performance in predicting cell states using only the six aforementioned numerical features. Out of 1046 candidate models, a multilayer perceptron was chosen with 0.91 live precision, 0.93 live recall, 0.92 live f value and 0.97 live area under the ROC curve when applied on standardized data. We discuss and highlight differences in classifier performance and compare the results to the standard practice of forward and side scatter gating, typically performed to select cells based on size and/or complexity. We demonstrate that our model, a ready-to-use module for any flow cytometry-based analysis, can provide automated, reliable, and stain-free classification of healthy and apoptotic cells using exclusively size and granularity information. 

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4. Mechanosensitivity Analysis of Breast Cancer Tumor Cells from Needle Biopsy

   Bedoya, Santiago; Ghosh, Deepraj; Dawson, Michelle (April 2018, The FASEB journal)

   Tumor stiffness has been associated with malignancy and increased risk for metastasis. Extensive research has been done investigating breast cancer cell lines’ responsiveness to surfaces of varying rigidities as well as examining the biophysical properties of breast cancer tumor samples. However, there is a critical gap regarding the relationship between cells’ mechanosensitivity in conjunction to biophysical properties of their extracellular matrix environment. To explore this relationship, we will analyze single-cell mechanosensitivity in comparison to tumor rigidity via shearwave ultrasound elastogrophy (SWE). Given the putative affiliation, we hypothesize that cells expressing invasive mechanosensitivity profiles will correlate with stiffer tumor regions. Using collagen gels containing different cell types, we derived biopsy-sized samples allowing us to optimize single-cell mechanosensitivity analysis. Cells were stained using different dyes corresponding to invasiveness. Subsequently, we analyzed their morphology. Morphological identification within organoid environments would allow for single-cell analysis without the aggression of tissue digestion, though preliminary results suggest high heterogeneity may not allow for confident cell identification solely on morphology. Thus, inquisition into cell viability and integrity was explored by analyzing the effects of tissue digestion with HyQtase on single-cells. Cell count and live-dead stain via flow cytometry allowed for analysis of single-cell viability. Lastly, cell integrity was evaluated by a 2D adhesion assay of isolated cells. The live/dead stain revealed that digestion resulted in isolation of approximately 10% of the original 500,000 cell population with 90–97% of the isolated population being live-cells (invasive and non-invasive respectively). Furthermore, the adhesion assay showed that these isolated single cells retained the ability to adhere to new surfaces, with no difference between the invasive and non-invasive cell types. These results show that cells are able to retain mechanosensitive properties following enzymatic digestion. However, they also suggest our digestion procedure is not aggressive enough to isolate invasive subpopulations that are more strongly imbedded in the original tissues. Development of these novel techniques will allow for accurate and confident analysis of precious human biopsy samples. Insight into the relationship between single-cell mechanosensitivity and tumor biophysical properties could elucidate pathways for metastasis inhibition and prevention. 

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5. In situ detection of dead cells from live cells via a DC plus low frequency AC resistive pulse sensor

   https://doi.org/10.1007/s10544-026-00797-y  

   Lybrook, Parker; Chen, Heyi; Barna, Emma; Brown, Jacob; Wong, Ashley; Ketchum, Joseph; Zhang, Ge; Zhe, Jiang (March 2026, Biomedical Microdevices)

   Abstract Differentiation and detection of live and dead cells are critical for assessing cell viability in biomedical research, evaluating drug efficacy, and monitoring cytotoxicity in therapeutic applications. We present a microfluidic sensor that consists of two successive resistive pulse sensing channels. An excitation signal composed of a low-frequency AC (75 kHz) component and a DC bias was used to measure four key parameters. Through the AC measurement, differences in cell impedance causes variations in phase angle and voltage peak. From the DC measurement, cell size can be inferred from the resistive pulse magnitude, and the cell’s zeta potential is represented by the transit time difference. Human umbilical vein endothelial cells (HUVECs) and human mesenchymal stem cells (hMSCs) were used to demonstrate the device’s utility. A soft margin support vector machine (SVM) was applied to define the decision boundary based on analysis of the four parameters. For both cell types, live and dead cells formed distinct clusters, achieving maximum classification accuracies of up to 100%. Additionally, HUVECs treated with either ethanol or staurosporine (STS) were classified with accuracies up to 100%. Compared to previous microfluidic resistive pulse sensor (RPS), this approach can determine cell viability without the need for complex labeling or modifications. Unlike impedance cytometry, it does not require high-frequency measurements, significantly reducing hardware requirements and data processing complexity, while still providing multiparametric measurements of cells. These measurements allow the use of soft SVM to classify cell groups with higher accuracy than single-parameter differentiation. 

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