Biology subjects

Skala, M. C.

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

34 records · Page 2Linked to original sources

bioRxiv2023

Optical coherence tomography of human fetal membrane sub-layers during dynamic loading

Fetal membranes have important mechanical and antimicrobial roles in maintaining pregnancy. However, the small thickness (<800 m) of fetal membranes places them outside the resolution limits of most ultrasound and magnetic resonance systems. Optical imaging methods like optical coherence tomography (OCT) have the potential to fill this resolution gap. Here, OCT and machine learning methods were developed to characterize the ex vivo properties of human fetal membranes under dynamic loading. A saline inflation test was incorporated into an OCT system, and tests were performed on n=33 and n=32 human samples obtained from labored and C-section donors, respectively. Fetal membranes were collected in near-cervical and near-placental locations. Histology, endogenous two photon fluorescence microscopy, and second harmonic generation microscopy were used to identify sources of contrast in OCT images of fetal membranes. A convolutional neural network was trained to automatically segment fetal membrane sub-layers with high accuracy (Dice coefficients >0.8). Intact amniochorion bilayer and separated amnion and chorion were individually loaded, and the amnion layer was identified as the load-bearing layer within intact fetal membranes for both labored and C-section samples, consistent with prior work. Additionally, the rupture pressure and thickness of the amniochorion bilayer from the near-placental region were greater than those of the near-cervical region for labored samples. This location-dependent change in fetal membrane thickness was not attributable to the load-bearing amnion layer. Finally, the initial phase of the loading curve indicates that amniochorion bilayer from the near-cervical region is strain-hardened compared to the near-placental region in labored samples. Overall, these studies fill a gap in our understanding of the structural and mechanical properties of human fetal membranes at high resolution under dynamic loading events.

biophysics↗

bioRxiv2023

cell-analysis-tools: an open-source library for single-cell analysis of multi-dimensional microscopy images

Single cell analysis of multi-dimensional microscopy images is repetitive, time consuming, and arduous. Numerous analysis steps are required to quantify and visualize cell heterogeneity and trends between experimental groups. The open-source community has created tools to facilitate this process. To further simplify analysis, we created a library of functions called cell-analysis-tools. This library includes functions that can streamline single-cell analysis for faster quality checking and automation. This library also includes example code with randomly generated data for dimensionality reduction [t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), Uniform Manifold Approximation and Projection (UMAP)] and machine learning models [random forest, support vector machine (SVM), linear regression] that scientists can swap with their own data to visualize trends. Lastly, this library includes template scripts for feature extraction that can help identify differences between experimental groups and cell heterogeneity within a group. This library can significantly decrease user time while increasing robustness and reproducibility of results.

cell biology↗

bioRxiv2023

Light sheet autofluorescence lifetime imaging with a single photon avalanche diode array

Single photon avalanche diode (SPAD) array sensors can increase the imaging speed for fluorescence lifetime imaging microscopy (FLIM) by transitioning from laser scanning to widefield geometries. While a SPAD camera in epi-fluorescence geometry enables widefield FLIM of fluorescently labeled samples, label-free imaging of single-cell autofluorescence is not feasible in an epi-fluorescence geometry because background fluorescence from out-of-focus features masks weak cell autofluorescence and biases lifetime measurements. Here, we address this problem by integrating the SPAD camera in a light sheet illumination geometry to achieve optical sectioning and limit out-of-focus contributions, enabling fast label-free FLIM of single-cell NAD(P)H autofluorescence. The feasibility of this NAD(P)H light sheet FLIM system was confirmed with time-course imaging of metabolic perturbations in pancreas cancer cells with 10 s integration times, and in vivo NAD(P)H light sheet FLIM was demonstrated with live neutrophil imaging in a zebrafish tail wound, also with 10 s integration times. Finally, the theoretical and practical imaging speeds for NAD(P)H FLIM were compared across laser scanning and light sheet geometries, indicating a 30X to 6X frame rate advantage for the light sheet compared to the laser scanning geometry. This light sheet system provides faster frame rates for 3D NAD(P)H FLIM for live cell imaging applications such as monitoring single cell metabolism and immune cell migration throughout an entire living organism.

bioengineering↗

bioRxiv2023

Autofluorescence lifetime imaging classifies human lymphocyte activation and subtype

New non-destructive tools are needed to reliably assess lymphocyte function for immune profiling and adoptive cell therapy. Optical metabolic imaging (OMI) is a label-free method that measures the autofluorescence intensity and lifetime of metabolic cofactors NAD(P)H and FAD to quantify metabolism at a single-cell level. Here, we investigate whether OMI can resolve metabolic changes between human quiescent versus IL4/CD40 activated B cells and IL12/IL15/IL18 activated memory-like NK cells. We found that quiescent B and NK cells were more oxidized compared to activated cells. Additionally, the NAD(P)H mean fluorescence lifetime decreased and the fraction of unbound NAD(P)H increased in the activated B and NK cells compared to quiescent cells. Machine learning classified B cells and NK cells according to activation state (CD69+) based on OMI parameters with up to 93.4% and 92.6% accuracy, respectively. Leveraging our previously published OMI data from activated and quiescent T cells, we found that the NAD(P)H mean fluorescence lifetime increased in NK cells compared to T cells, and further increased in B cells compared to NK cells. Random forest models based on OMI classified lymphocytes according to subtype (B, NK, T cell) with 97.8% accuracy, and according to activation state (quiescent or activated) and subtype (B, NK, T cell) with 90.0% accuracy. Our results show that autofluorescence lifetime imaging can accurately assess lymphocyte activation and subtype in a label-free, non-destructive manner. TeaserLabel-free optical imaging can assess the metabolic state of lymphocytes on a single-cell level in a touch-free system.

bioengineering↗

bioRxiv2022

Autofluorescence is a biomarker of neural stem cell activation state

Neural stem cells (NSCs) in the adult brain are primarily quiescent but can activate and enter the cell cycle to produce newborn neurons. NSC quiescence can be regulated by disease, injury, and age, however our understanding of NSC quiescence is limited by technical limitations imposed by the bias of markers used to isolate each population of NSCs and the lack of live-cell labeling strategies. Fluorescence lifetime imaging (FLIM) of autofluorescent metabolic cofactors has previously been used in other cell types to study shifts in cell states driven by metabolic remodeling that change the optical properties of these endogenous fluorophores. Here we asked whether autofluorescence could be used to discriminate NSC activation state. We found that quiescent NSCs (qNSCs) and activated NSCs (aNSCs) each have unique autofluorescence intensity and fluorescence lifetime profiles. Additionally, qNSCs specifically display an enrichment of a specific autofluorescent signal localizing to lysosomes that is highly predictive of cell state. These signals can be used as a graded marker of NSC quiescence to predict cell behavior and track the dynamics of quiescence exit at single cell resolution in vitro and in vivo. Through coupling autofluorescence imaging with single-cell RNA sequencing in vitro and in vivo, we provide a high-resolution resource revealing transcriptional features linked to rapid NSC activation and deep quiescence. Taken together, we describe a single-cell resolution, non-destructive, live-cell, label-free strategy for measuring NSC activation state in vitro and in vivo and use this tool to expand our understanding of adult neurogenesis.

neuroscience↗

bioRxiv2022

Differential metabolic adaptations define responses of winner and loser oncogenic mutant stem cells in skin epidermis in vivo

Skin epithelial stem cells detect and correct aberrancies induced by oncogenic mutations. Different oncogenes invoke different mechanisms of epithelial tolerance: while wild-type cells outcompete {beta}-catenin-Gain-of-Function ({beta}catGOF) mutant cells, HrasG12V mutant cells outcompete wild-type cells1,2. Here we ask how metabolic states change as wild-type stem cells interface with mutant cells, and how this ultimately drives different cell competition outcomes. By adapting our live-imaging platform to track endogenous redox ratio (NAD(P)H/FAD) with single cell resolution in the same mice over time, we show that wild-type epidermal stem cells maintain robust redox ratio despite their heterogeneous cell cycle states. We discover that both {beta}catGOF and HrasG12V models lead to a rapid drop in redox ratios. However, the "winner" cells in each model (wild-type in {beta}catGOF and mutant in HrasG12V), rapidly recover their redox ratios, irrespective of the mutation induced. Using mass spectrometry (13C-LC-MS/MS)3, we find that both mutants increase flux through the oxidative tricarboxylic acid cycle, but the "winner" HrasG12V cells and the "loser" {beta}catGOF cells modulate glycolytic flux differently. Hence, we reveal the metabolic adaptations that define the hallmarks of winners and losers during cell competition in vivo and uncover the nodes of regulation unique to each cell fate.

cancer biology↗

bioRxiv2021

Label-free imaging to track reprogramming of human somatic cells

The process of reprogramming patient samples to human induced pluripotent stem cells (iPSCs) is stochastic, asynchronous, and inefficient leading to a heterogeneous population of cells. Here, we track the reprogramming status of single patient-derived cells during reprogramming with label-free live-cell imaging of cellular metabolism and nuclear morphometry to identify high-quality iPSCs. Erythroid progenitor cells (EPCs) isolated from human peripheral blood showed distinct patterns of autofluorescence lifetime for the reduced form of nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] and flavin adenine dinucleotide (FAD) during reprogramming. Random forest models classified starting EPCs, partially-reprogrammed intermediate cells, and iPSCs with [~]95% accuracy. Reprogramming trajectories resolved at the single cell level indicated significant reprogramming heterogeneity along different branches of cell state. This combination of micropatterning, autofluorescence imaging, and machine learning provides a unique non-destructive method to assess the quality of iPSCs in real-time for various applications in regenerative medicine, cell therapy biomanufacturing, and disease modeling.

bioengineering↗

bioRxiv2021

Integrating Subclonal Response Heterogeneity to Define Cancer Organoid Therapeutic Sensitivity

Tumor heterogeneity is predicted to confer inferior clinical outcomes, however modeling heterogeneity in a manner that still represents the tumor of origin remains a formidable challenge. Sequencing technologies are limited in their ability to identify rare subclonal populations and predict response to the multitude of available treatments for patients. Patient-derived organotypic cultures have significantly improved the modeling of cancer biology by faithfully representing the molecular features of primary malignant tissues. Patient-derived cancer organoid (PCO) cultures contain numerous individual organoids with the potential to recapitulate heterogeneity, though PCOs are most commonly studied in bulk ignoring any diversity in the molecular profile or treatment response. Here we demonstrate the advantage of evaluating individual PCOs in conjunction with cellular level optical metabolic imaging to characterize the largely ignored heterogeneity within these cultures to predict clinical therapeutic response, identify subclonal populations, and determine patient specific mechanisms of resistance.

cancer biology↗

bioRxiv2021

Impact of baseline culture conditions of mouse-derived cancer organoids when determining therapeutic response and tumor heterogeneity

Representative models are needed to screen new therapies for patients with cancer. Cancer organoids are a leap forward as a culture model that faithfully represents the disease. Mouse-derived cancer organoids (MDCOs) are becoming increasingly popular, however there has yet to be a standardized method to assess therapeutic response and identify subpopulation heterogeneity. There are multiple factors unique to organoid culture that could affect how therapeutic response and MDCO heterogeneity are assessed. Here we describe an analysis of nearly 3,500 individual MDCOs where individual organoid morphologic tracking was performed. Change in MDCO diameter was assessed in the presence of control media or targeted therapies. Individual organoid tracking was identified to be more sensitive to treatment response than well-level assessment. The impact of different generations of mice of the same genotype, different regions of the colon, and organoid specific characteristics including baseline size, passage number, plating density, and location within the matrix were examined. Only the starting size of the MDCO altered the subsequent growth. Here we establish organoid culture parameters for individual organoid morphologic tracking to determine therapeutic response and growth/response heterogeneity for translational studies using murine colorectal cancer organoids.

cancer biology↗

bioRxiv2020

Patient-derived cancer organoid tracking with widefield one-photon redox imaging to assess treatment response

MotivationAccessible tools are needed for rapid, non-destructive imaging of patient-derived cancer organoid (PCO) treatment response to accelerate drug discovery and streamline treatment planning for individual patients. AimSegment and track individual PCOs with widefield one-photon redox imaging to extract morphological and metabolic variables of treatment response. ApproachRedox imaging of the endogenous fluorophores, NAD(P)H and FAD, was used to monitor the metabolic state and morphology of PCOs. Redox imaging was performed on a widefield one-photon epifluorescence microscope to evaluate drug response in two colorectal PCO lines. An automated image analysis framework was developed to track PCOs across multiple time points over 48 hours. Variables quantified for each PCO captured metabolic and morphological response to drug treatment, including the optical redox ratio and organoid area. ResultsThe optical redox ratio (NAD(P)H/(FAD+NAD(P)H)) was independent of PCO morphology pre-tieatment. Drugs that induced cell death decreased the optical redox ratio and growth rate compared to control. Multivariate analysis of redox and morphology variables identified distinct PCO sub-populations. Single-organoid tracking improved sensitivity to drug treatment compared to pooled organoid analysis. ConclusionWidefield one-photon redox imaging can monitor metabolic and morphological changes on a single organoid-level, providing an accessible, non-destructive tool to screen drugs in patient-matched samples.

bioengineering↗

bioRxiv2020

Interactions with stromal cells promote a more oxidized cancer cell redox state in pancreatic tumors

Access to electron acceptors supports oxidized biomass synthesis and can be limiting for cancer cell proliferation, but how cancer cells overcome this limitation in tumors is incompletely understood. Non-transformed cells in tumors can help cancer cells overcome metabolic limitations, particularly in pancreatic cancer, where pancreatic stellate cells (PSCs) promote cancer cell proliferation and tumor growth. However, whether PSCs affect the redox state of cancer cells is not known. By taking advantage of the endogenous fluorescence properties of reduced nicotinamide adenine dinucleotide cofactors and oxidized flavin adenine dinucleotide, we use optical imaging to assess the redox state of pancreatic cancer cells and PSCs and find that the redox state of cancer cells is more reduced while the redox state of PSCs is more oxidized. Direct interactions between PSCs and cancer cells promote a more oxidized state in cancer cells, suggesting that metabolic interactions between cancer cells and PSCs is a mechanism to overcome the redox limitations of cell proliferation in pancreatic cancer.

cancer biology↗

bioRxiv2020

Adaptable pulsatile flow generated by quantitative imaging of stem-cell derived cardiomyocytes for disease modeling

Endothelial cells (EC) in vivo are continuously exposed to a mechanical microenvironment from blood flow, and fluidic shear stress plays an important role in EC behavior. New approaches to generate physiologically and pathologically relevant pulsatile flows are needed to understand EC behavior under different shear stress regimes. Here, we demonstrate an adaptable pump (Adapt-Pump) platform for generating pulsatile flows via quantitative imaging of human pluripotent stem cell-derived cardiac spheroids (CS). Pulsatile flows generated from the Adapt-Pump system can recapitulate unique CS contraction characteristics, accurately model responses to clinically relevant drugs, and simulate CS contraction changes in response to fluidic mechanical stimulation. We discovered that ECs differentiated under a long QT syndrome derived pathological pulsatile flow exhibit abnormal EC monolayer organization. This Adapt-Pump platform provides a powerful tool for modeling the cardiovascular system and improving our understanding of EC behavior under different mechanical microenvironments.

bioengineering↗

bioRxiv2019

Classifying T cell activity in autofluorescence intensity images with convolutional neural networks

The importance of T cells in immunotherapy has motivated developing technologies to better characterize T cells and improve therapeutic efficacy. One specific objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states of individual cells in a non-destructive manner by detecting endogenous changes in metabolic co-enzymes such as NAD(P)H. However, recognizing robust patterns of T cell activity is computationally challenging in the absence of exogenous labels or information-rich autofluorescence lifetime measurements. We demonstrate that advanced machine learning can accurately classify T cell activity from NAD(P)H intensity images and that those image-based signatures transfer across human donors. Using a dataset of 8,260 cropped single-cell images from six donors, we meticulously evaluate multiple machine learning models. These range from traditional models that represent images using summary statistics or extract image features with CellProfiler to deep convolutional neural networks (CNNs) pre-trained on general non-biological images. Adapting pre-trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. However, we observe that fine-tuning all layers of the pre-trained CNN does not provide a classification performance boost commensurate with the additional computational cost. Our software detailing our image processing and model training pipeline is available as Jupyter notebooks at https://github.com/gitter-lab/t-cell-classification.

bioinformatics↗

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↗