droplet based microfluidics platform  (10X Genomics)

Journal: BioImpacts : BI

Article Title: Microfluidics as a promising technology for personalized medicine

doi: 10.34172/bi.29944

Figure Lengend Snippet: Microfluidic-based studies for biomolecular detection

Article Snippet: Porous membrane based microfluidic platforms also can simply separate microvesicles from biofluids.

Techniques: Amplification, Labeling, Isolation, Binding Assay, Hybridization, SPR Assay, Control

Journal: BioImpacts : BI

Article Title: Microfluidics as a promising technology for personalized medicine

doi: 10.34172/bi.29944

Figure Lengend Snippet: Microfluidic-based studies for drug screening

Article Snippet: Porous membrane based microfluidic platforms also can simply separate microvesicles from biofluids.

Techniques: Isolation, Drug discovery, Microscopy

Journal: BioImpacts : BI

Article Title: Microfluidics as a promising technology for personalized medicine

doi: 10.34172/bi.29944

Figure Lengend Snippet: Cancer on a chip studies

Article Snippet: Porous membrane based microfluidic platforms also can simply separate microvesicles from biofluids.

Techniques: Membrane, Cell Culture, Activation Assay, In Vitro, Shear

Journal: BioImpacts : BI

Article Title: Microfluidics as a promising technology for personalized medicine

doi: 10.34172/bi.29944

Figure Lengend Snippet: Organ-on-a -chip studies

Article Snippet: Porous membrane based microfluidic platforms also can simply separate microvesicles from biofluids.

Techniques: Cell Culture, Diffusion-based Assay, Shear, Functional Assay, Membrane, Construct, Derivative Assay, Generated, Polymer, Fluorescence

Journal: Translational Lung Cancer Research

Article Title: Quantitative peripheral live single T-cell dynamic polyfunctionality profiling predicts lung cancer checkpoint immunotherapy treatment response and clinical outcomes

doi: 10.21037/tlcr-24-260

Figure Lengend Snippet: IsoLight single T-cell live functional immune proteomics profiling workflow.

Article Snippet: In this proof-of-concept analysis, we adopted a microfluidics-based multiplexed lab-on-chip proteomics assay platform, IsoLight (Bruker Cellular Analysis, Branford, CT, USA; formerly IsoPlexis), to functionally interrogate live peripheral T-lymphocyte subsets at the single-cell level in a discovery study of T-lymphocytes polyfunctionality as a potential predictive biomarker for ICI treatment response and clinical outcomes correlation in NSCLC.

Techniques: Functional Assay

Journal: bioRxiv

Article Title: Single-Cell Data Integration and Cell Type Annotation through Contrastive Adversarial Open-set Domain Adaptation

doi: 10.1101/2024.10.04.616599

Figure Lengend Snippet: A) Comparison of SAFAARI’s performance with the selected reference-based cell-type annotation models in both open-set and closed-set settings. The scRNA-seq data from eight different tissues in the Tabula Muris cell atlas was obtained where the gene counts were derived using two techniques: 10x Genomics and FACS-based cell capture in plates (FACS). For the performance assessment, either FACS or 10x was considered as the source dataset, and the other as the target dataset, to evaluate reference-based cell type annotation or label transfer in the presence of a technology-based domain-shift or batch effect. Two scenarios were considered: the closed-set, where only cell types common to both source and target datasets were included, and the open-set, where the target dataset contained an unknown cell type not present in the source dataset . B) Heatmap representing the confusion matrix across eight tissues (target: FACS), showing cell-type-specific annotation performance. Columns represent the actual cell labels, while rows show the predicted cell labels. The cell type coloured in navy blue represents the unknown cell type whose instances were removed from the source dataset. Colours in the viridis palette and indicate the proportion of cells relative to the sum of the column (i.e., values across columns should add up to 1.0). This represents the proportion of correct classifications (diagonal values) and misclassifications for each particular cell type represented by the column names. C) UMAP of open-set Label transfer result of SAFAARI on four human pancreas datasets generated with different technologies, including microfluidic (Fluidigm C), droplet-based (InDrops) and plate-based scRNA-seq (CEL-seq2, Smart-seq2) as detailed in . It demonstrates SAFAARI’s superior batch mixing, cell separation and unknown cell type detection.

Article Snippet: These methods range from microfluidic droplet-based platforms (such as 10x Genomics Chromium, Drop-seq, and inDrops) to plate-based scRNA-seq technologies like Smart-seq, Smart-seq2, and Smart-seq3, resulting in substantial heterogeneity across datasets.

Techniques: Comparison, Derivative Assay, Generated