Instant processing of large-scale image data with FACT, a real-time cell segmentation and tracking algorithm

Quantifying cellular characteristics from a large heterogeneous population is essential to identify rare, disease-driving cells. A recent development in the combination of high-throughput screening microscopy with single-cell profiling provides an unprecedented opportunity to decipher disease-driving phenotypes. Accurately and instantly processing large amounts of image data, however, remains a technical challenge when an analysis output is required minutes after data acquisition. Here, we present fast and accurate real-time cell tracking (FACT). FACT can segment ∼20,000 cells in an average of 2.5 s (1.9–93.5 times faster than the state of the art). It can export quantifiable features minutes after data acquisition (independent of the number of acquired image frames) with an average of 90%–96% precision. We apply FACT to identify directionally migrating glioblastoma cells with 96% precision and irregular cell lineages from a 24 h movie with an average F1 score of 0.91.</p

Instant processing of large-scale image data with FACT, a real-time cell segmentation and tracking algorithm

Quantifying cellular characteristics from a large heterogeneous population is essential to identify rare, disease-driving cells. A recent development in the combination of high-throughput screening microscopy with single-cell profiling provides an unprecedented opportunity to decipher disease-driving phenotypes. Accurately and instantly processing large amounts of image data, however, remains a technical challenge when an analysis output is required minutes after data acquisition. Here, we present fast and accurate real-time cell tracking (FACT). FACT can segment ∼20,000 cells in an average of 2.5 s (1.9–93.5 times faster than the state of the art). It can export quantifiable features minutes after data acquisition (independent of the number of acquired image frames) with an average of 90%–96% precision. We apply FACT to identify directionally migrating glioblastoma cells with 96% precision and irregular cell lineages from a 24 h movie with an average F1 score of 0.91.</p

Incoherently pumped continuous wave optical parametric oscillator broadened by non-collinear phasematching

In this paper, we report on a singly resonant optical parametric oscillator (OPO) pumped by an amplified spontaneous emission (ASE) source. The pump focusing conditions allow non-collinear phasematching, which resulted in a 230 nm (190 cm$^{-1}$) spectral bandwidth. Calculations indicate that such phasematching schemes may be used to further broaden OPO spectral bandwidths.Comment: 7 pages 4 figure

Spatially Annotated Single Cell Sequencing for Unraveling Intratumor Heterogeneity

Intratumor heterogeneity is a major obstacle to effective cancer treatment. Current methods to study intratumor heterogeneity using single-cell RNA sequencing (scRNA-seq) lack information on the spatial organization of cells. While state-of-the art spatial transcriptomics methods capture the spatial distribution, they either lack single cell resolution or have relatively low transcript counts. Here, we introduce spatially annotated single cell sequencing, based on the previously developed functional single cell sequencing (FUNseq) technique, to spatially profile tumor cells with deep scRNA-seq and single cell resolution. Using our approach, we profiled cells located at different distances from the center of a 2D epithelial cell mass. By profiling the cell patch in concentric bands of varying width, we showed that cells at the outermost edge of the patch responded strongest to their local microenvironment, behaved most invasively, and activated the process of epithelial-to-mesenchymal transition (EMT) to migrate to low-confluence areas. We inferred cell-cell communication networks and demonstrated that cells in the outermost ∼10 cell wide band, which we termed the invasive edge, induced similar phenotypic plasticity in neighboring regions. Applying FUNseq to spatially annotate and profile tumor cells enables deep characterization of tumor subpopulations, thereby unraveling the mechanistic basis for intratumor heterogeneity