Adaptive settings for the nearest-neighbor particle tracking algorithm

Machine learning predictions of T cell antigen specificity from intracellular calcium dynamics

Adoptive T cell therapies rely on the production of T cells with an antigen receptor that directs their specificity toward tumor-specific antigens. Methods for identifying relevant T cell receptor (TCR) sequences, predominantly achieved through the enrichment of antigen-specific T cells, represent a major bottleneck in the production of TCR-engineered cell therapies. Fluctuation of intracellular calcium is a proximal readout of TCR signaling and candidate marker for antigen-specific T cell identification that does not require T cell expansion; however, calcium fluctuations downstream of TCR engagement are highly variable. We propose that machine learning algorithms may allow for T cell classification from complex datasets such as polyclonal T cell signaling events. Using deep learning tools, we demonstrate accurate prediction of TCR-transgenic CD8+ T cell activation based on calcium fluctuations and test the algorithm against T cells bearing a distinct TCR as well as polyclonal T cells. This provides the foundation for an antigen-specific TCR sequence identification pipeline for adoptive T cell therapies.

An Image Processing Algorithm for Facile and Reproducible Quantification of Vomocytosis

Vomocytosis is a process that occurs when internalized fungal pathogens escape from phagocytes without compromising the viability of the pathogen and the host cell. Manual quantification of time-lapse microscopy videos is currently used as the standard to study pathogen behavior and vomocytosis incidence. However, human-driven quantification of vomocytosis (and the closely related phenomenon, exocytosis) is incredibly burdensome, especially when a large volume of cells and interactions needs to be analyzed. In this study, we designed a MATLAB algorithm that measures the extent of colocalization between the phagocyte and fungal cell (Cryptococcus neoformans; CN) and rapidly reports the occurrence of vomocytosis in a high throughput manner. Our code processes multichannel, time-lapse microscopy videos of cocultured CN and immune cells that have each been fluorescently stained with unique dyes and provides quantitative readouts of the spatiotemporally dynamic process that is vomocytosis. This study also explored metrics, such as the rate of change of pathogen colocalization with the host cell, that could potentially be used to predict vomocytosis occurrence based on the quantitative data collected. Ultimately, the algorithm quantifies vomocytosis events and reduces the time for video analysis from over 1 h to just 10 min, a reduction in labor of 83%, while simultaneously minimizing human error. This tool significantly minimizes the vomocytosis analysis pipeline, accelerates our ability to elucidate unstudied aspects of this phenomenon, and expedites our ability to characterize CN strains for the study of their epidemiology and virulence.

Migration speed of captured breast cancer subpopulations correlates with metastatic fitness

The genetic alterations contributing to migration proficiency, a phenotypic hallmark of metastatic cells required for colonizing distant organs, remain poorly defined. Here, we used single-cell magneto-optical capture (scMOCa) to isolate fast cells from heterogeneous human breast cancer cell populations, based on their migratory ability alone. We show that captured fast cell subpopulations retain higher migration speed and focal adhesion dynamics over many generations as a result of a motility-related transcriptomic profile. Upregulated genes in isolated fast cells encoded integrin subunits, proto-cadherins and numerous other genes associated with cell migration. Dysregulation of several of these genes correlates with poor survival outcomes in people with breast cancer, and primary tumors established from fast cells generated a higher number of circulating tumor cells and soft tissue metastases in pre-clinical mouse models. Subpopulations of cells selected for a highly migratory phenotype demonstrated an increased fitness for metastasis.

A computer vision approach for analyzing label free leukocyte trafficking dynamics on a microvascular mimetic

High-content imaging techniques in conjunction with in vitro microphysiological systems (MPS) allow for novel explorations of physiological phenomena with a high degree of translational relevance due to the usage of human cell lines. MPS featuring ultrathin and nanoporous silicon nitride membranes (µSiM) have been utilized in the past to facilitate high magnification phase contrast microscopy recordings of leukocyte trafficking events in a living mimetic of the human vascular microenvironment. Notably, the imaging plane can be set directly at the endothelial interface in a µSiM device, resulting in a high-resolution capture of an endothelial cell (EC) and leukocyte coculture reacting to different stimulatory conditions. The abundance of data generated from recording observations at this interface can be used to elucidate disease mechanisms related to vascular barrier dysfunction, such as sepsis. The appearance of leukocytes in these recordings is dynamic, changing in character, location and time. Consequently, conventional image processing techniques are incapable of extracting the spatiotemporal profiles and bulk statistics of numerous leukocytes responding to a disease state, necessitating labor-intensive manual processing, a significant limitation of this approach. Here we describe a machine learning pipeline that uses a semantic segmentation algorithm and classification script that, in combination, is capable of automated and label-free leukocyte trafficking analysis in a coculture mimetic. The developed computational toolset has demonstrable parity with manually tabulated datasets when characterizing leukocyte spatiotemporal behavior, is computationally efficient and capable of managing large imaging datasets in a semi-automated manner.

Extending resolution within a single imaging frame

The resolution of fluorescence microscopy images is limited by the physical properties of light. In the last decade, numerous super-resolution microscopy (SRM) approaches have been proposed to deal with such hindrance. Here we present Mean-Shift Super Resolution (MSSR), a new SRM algorithm based on the Mean Shift theory, which extends spatial resolution of single fluorescence images beyond the diffraction limit of light. MSSR works on low and high fluorophore densities, is not limited by the architecture of the optical setup and is applicable to single images as well as temporal series. The theoretical limit of spatial resolution, based on optimized real-world imaging conditions and analysis of temporal image stacks, has been measured to be 40 nm. Furthermore, MSSR has denoising capabilities that outperform other SRM approaches. Along with its wide accessibility, MSSR is a powerful, flexible, and generic tool for multidimensional and live cell imaging applications.

Connecting the Non-Brownian Dots: Increasing Near-Neighbor Particle-Tracking Efficiency by Coordinate System Manipulation

The nearest-neighbor algorithm (N-N) for single particle tracking (SPT) is widely employed for studying the deformation and mechanics of soft materials, or to detect flow in microfluidic systems. However, this algorithm may not perform well under certain conditions of oscillatory or directed motion of the studied tracers. Here, a method is presented with the goal of improving the performance of NN-SPT algorithms when studying directed and oscillatory motions. Specifically, the approach applies a change-of-basis matrix to the detected particles positions, prior to the calculations made by the NN-SPT algorithm. The presented results demonstrate the superior tracking efficiency when analyzing these systems, manifested via lower tracking mismatches and less spurious results than the original N-N algorithm.

C3a elicits unique migratory responses in immature low-density neutrophils

Neutrophils represent the immune system's first line of defense and are rapidly recruited into inflamed tissue. In cancer associated inflammation, phenotypic heterogeneity has been ascribed to this cell type, whereby neutrophils can manifest anti- or pro-metastatic functions depending on the cellular/micro-environmental context. Here, we demonstrate that pro-metastatic immature low-density neutrophils (iLDNs) more efficiently accumulate in the livers of mice bearing metastatic lesions compared with anti-metastatic mature high-density neutrophils (HDNs). Transcriptomic analyses reveal enrichment of a migration signature in iLDNs relative to HDNs. We find that conditioned media derived from liver-metastatic breast cancer cells, but not lung-metastatic variants, specifically induces chemotaxis of iLDNs and not HDNs. Chemotactic responses are due to increased surface expression of C3aR in iLDNs relative to HDNs. In addition, we detect elevated secretion of cancer-cell derived C3a from liver-metastatic versus lung-metastatic breast cancer cells. Perturbation of C3a/C3aR signaling axis with either a small molecule inhibitor, SB290157, or reducing the levels of secreted C3a from liver-metastatic breast cancer cells by short hairpin RNAs, can abrogate the chemotactic response of iLDNs both in vitro and in vivo, respectively. Together, these data reveal novel mechanisms through which iLDNs prefentially accumulate in liver tissue harboring metastases in response to tumor-derived C3a secreted from the liver-aggressive 4T1 breast cancer cells.

Quantitative label-free single cell tracking in 3D biomimetic matrices

Live cell imaging enables an observation of cell behavior over a period of time and is a growing field in modern cell biology. Quantitative analysis of the spatio-temporal dynamics of heterogeneous cell populations in three-dimensional (3D) microenvironments contributes a better understanding of cell-cell and cell-matrix interactions for many biomedical questions of physiological and pathological processes. However, current live cell imaging and analysis techniques are frequently limited by non-physiological 2D settings. Furthermore, they often rely on cell labelling by fluorescent dyes or expression of fluorescent proteins to enhance contrast of cells, which frequently affects cell viability and behavior of cells. In this work, we present a quantitative, label-free 3D single cell tracking technique using standard bright-field microscopy and affordable computational resources for data analysis. We demonstrate the efficacy of the automated method by studying migratory behavior of a large number of primary human macrophages over long time periods of several days in a biomimetic 3D microenvironment. The new technology provides a highly affordable platform for long-term studies of single cell behavior in 3D settings with minimal cell manipulation and can be implemented for various studies regarding cell-matrix interactions, cell-cell interactions as well as drug screening platform for primary and heterogeneous cell populations.

A Haptotaxis Assay for Neutrophils using Optical Patterning and a High-content Approach

Neutrophil recruitment guided by chemotactic cues is a central event in host defense against infection and tissue injury. While the mechanisms underlying neutrophil chemotaxis have been extensively studied, these are just recently being addressed by using high-content approaches or surface-bound chemotactic gradients (haptotaxis) in vitro. Here, we report a haptotaxis assay, based on the classic under-agarose assay, which combines an optical patterning technique to generate surface-bound formyl peptide gradients as well as an automated imaging and analysis of a large number of migration trajectories. We show that human neutrophils migrate on covalently-bound formyl-peptide gradients, which influence the speed and frequency of neutrophil penetration under the agarose. Analysis revealed that neutrophils migrating on surface-bound patterns accumulate in the region of the highest peptide concentration, thereby mimicking in vivo events. We propose the use of a chemotactic precision index, gyration tensors and neutrophil penetration rate for characterizing haptotaxis. This high-content assay provides a simple approach that can be applied for studying molecular mechanisms underlying haptotaxis on user-defined gradient shape.