Biology subjects

Yamawaki, T. M.

Publications and source records attributed to Yamawaki, T. M..

3 recordsLinked to original sources

bioRxiv2023

AN ARTIFICIAL INTELLIGENCE FOR RAPID IN-LINE LABEL-FREE HUMAN PLURIPOTENT STEM CELL COUNTING AND QUALITY ASSESSMENT

The current state-of-the-art in hPSC culture is a bespoke and user-dependent process limiting the scale and complexity of the experiments performed and introducing operator-to-operator and day-to-day variation. Artificial intelligence (AI) offers the speed and flexibility to bridge the gap between a human-dependent process and industrial-scale automation. We evaluated an AI approach for counting exact cell numbers of undifferentiated human induced pluripotent stem cells in brightfield images for automating hPSC culture. The neural network generates a topological density map for accurate cell counts. We found that the image-based AI algorithm can determine a precise number of hPSCs and is superior to fluorescence-labeled object detection; the algorithm can ignore well edges, meniscus effects, and dust, achieving an average error of 5.6%. We have built a prototype capable of making a go/no go decision for stem cell passaging to perform 26,400 individual well-level counts from 422,400 images in 12 hours at low cost.

cell biology↗

bioRxiv2020

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BackgroundElucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. ResultsHere, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluate methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5 v1 and 3 v3 methods. We demonstrate that these methods have fewer drop-out events which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. ConclusionOverall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

immunology↗

bioRxiv2020

Spatial tissue profiling by imaging-free molecular tomography

Genomics techniques are currently being adapted to provide spatially resolved omics profiling. However, the adaptation of each new method typically requires the setup of specific detection strategies or specialized instrumentation. A generic approach to spatially resolve different types of high throughput data is missing. Here, we describe an imaging-free framework to localize high throughput readouts within a tissue by combining compressive sampling and image reconstruction. We implemented this framework to transform a low-input RNA sequencing protocol into an imaging-free spatial transcriptomics technique (STRP-seq) and validated this method with a transcriptome profiling of the murine brain. To verify the broad applicability of STRP-seq, we applied the technique on the brain of the Australian bearded dragon Pogona vitticeps. Our results reveal the molecular anatomy of the telencephalon of this lizard, providing evidence for a marked regionalization of the reptilian pallium and subpallium. Overall, the proposed framework constitutes a new approach that allows upgrading in a generic fashion conventional genomic assays to spatially resolved techniques.

genomics↗