Biology subjects

Skala, M. C.

Publications and source records attributed to Skala, M. C..

3 recordsLinked to original sources

bioRxiv2019

Graphene nanoflakes for acute manipulation of membrane cholesterol and transmembrane signaling

Cholesterol is one of the most essential lipids in eukaryotic cell membranes. However, acute and selective manipulation of membrane cholesterol remains challenging. Here, we report that graphene nanoflakes (GNFs) insert into the plasma membrane and directly interact with cholesterol, resulting in acute cholesterol enrichment - and thus structural and functional changes. Using two representative cell preparations, we explore the utility of GNFs in modifying cell communication pathways sensitive to membrane cholesterol. In fibroblasts, GNFs enhance ATP-induced intracellular Ca2+-release by allosteric facilitation of P2Y receptors, a subtype of G protein-coupled receptors, in a cholesterol-dependent manner. In neurons, which possess higher membrane cholesterol levels than most cell types, GNFs further increase cholesterol. Consequently, GNFs change membrane fluidity, especially at synaptic boutons, and potentiate neurotransmitter release by accelerating synaptic vesicle turnover. Together, our results provide a molecular explanation for graphenes cellular impacts and demonstrate its potential for membrane-oriented engineering of cell signaling.

cell biology↗

bioRxiv2019

Optical Metabolic Imaging of Heterogeneous Drug Response in Pancreatic Cancer Patient Organoids

New tools are needed to match pancreatic cancer patients with effective treatments. Patient-derived organoids offer a high-throughput platform to personalize treatments and discover novel therapies. Currently, methods to evaluate drug response in organoids are limited because they cannot be completed in a clinically relevant time frame, only evaluate response at one time point, and most importantly, overlook cellular heterogeneity. In this study, non-invasive optical metabolic imaging (OMI) of cellular heterogeneity in organoids was evaluated as a predictor of clinical treatment response. Organoids were generated from fresh patient tissue samples acquired during surgery and treated with the same drugs as the patient's prescribed adjuvant treatment. OMI measurements of heterogeneity in response to this treatment were compared to later patient response, specifically to the time to recurrence following surgery. OMI was sensitive to patient-specific treatment response in as little as 24 hours. OMI distinguished subpopulations of cells with divergent and dynamic responses to treatment in living organoids without the use of labels or dyes. OMI of organoids agreed with long-term therapeutic response in patients. With these capabilities, OMI could serve as a sensitive high-throughput tool to identify optimal therapies for individual pancreatic cancer patients, and to develop new effective therapies that address cellular heterogeneity in pancreatic cancer.

bioengineering↗

bioRxiv2019

Label-free Method for Classification of T cell Activation

T cells have a range of cytotoxic and immune-modulating functions, depending on activation state and subtype. However, current methods to assess T cell function use exogenous labels that often require cell permeabilization, which is limiting for time-course studies of T cell activation and non-destructive quality control of immunotherapies. Label-free optical imaging is an attractive solution. Here, we use autofluorescence imaging of NAD(P)H and FAD, co-enzymes of metabolism, to quantify optical imaging endpoints in quiescent and activated T cells. Machine learning classification models were developed for label-free, non-destructive determination of T cell activation state. T cells were isolated from the peripheral blood of human donors, and a subset were activated with a tetrameric antibody against CD2/CD3/CD28 surface ligands. NAD(P)H and FAD autofluorescence intensity and lifetime of the T cells were imaged using a multiphoton fluorescence lifetime microscope. Significant differences in autofluorescence imaging end-points were observed between quiescent and activated T cells. Feature selection methods revealed that the contribution of the short NAD(P)H lifetime (1) is the most important feature for classification of activation state, across multiple donors and T cell subsets. Logistic regression models achieved 97-99% accuracy for classification of T cell activation from the autofluorescence imaging endpoints. Additionally, autofluorescence imaging revealed NAD(P)H and FAD autofluorescence differences between CD3+CD8+ and CD3+CD4+ T cells, and random forest models of the autofluorescence imaging endpoints achieved 97+% accuracy for four-group classification of quiescent and activated CD3+CD8+ and CD3+CD4+ T cells. Altogether these results indicate that autofluorescence imaging of NAD(P)H and FAD is a powerful method for label-free, non-destructive determination of T cell activation and subtype, which could have important applications for the treatment of cancer, autoimmune, infectious, and other diseases.

bioengineering↗