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c1 microfluidics based platform  (fluidigm)


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    Structured Review

    fluidigm c1 microfluidics based platform
    C1 Microfluidics Based Platform, supplied by fluidigm, used in various techniques. Bioz Stars score: 96/100, based on 2081 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/microfluidic-based+platforms/C1/pmc12422205-101-6-9
    Average 96 stars, based on 2081 article reviews
    c1 microfluidics based platform - by Bioz Stars, 2026-09
    96/100 stars

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    Related Articles

    Sequencing:

    Article Title: LMO1 expression in neuroblastoma cells reprograms tumor-associated macrophages to promote metastasis
    Article Snippet: Before CM treatment, approximately 1 × 10 6 U937 cells were seeded in 6-well plates and differentiated to macrophages with 20 ng/mL phorbol 12-myristate 13-acetate (PMA) (Sigma-Aldrich, #P1585) for 24 h. Differentiated U937 cells were rinsed with 1× PBS prior to treatment with collected CM for 48 h. .. A Fluidigm C1 system was used to capture single cells dissociated from zebrafish tumors. cDNA libraries from single cells were prepared using the SMART-seq strategy and subjected to Illumina Hi-Seq sequencing at the Harvard Medical School Biopolymers Facility. ..

    Article Title: Genomic Sequencing from Sanger to Next-Generation Sequencing: Historical Context, Comparative Advances, and Prospects for Next-Generation Phenomics.
    Article Snippet: DNA sequencing has revolutionized biological and biomedical research, offering profound insights into genome organization, function, and variability.. From the pioneering Sanger capillary electrophoresis method to the advent of next-generation sequencing, the field has evolved toward unprecedented speed, scalability, and cost decreases over the years.. These advancements have enabled diverse applications across genomics, transcriptomics, metagenomics, epigenomics, and precision medicine, powering global initiatives such as the Human Genome Project, the Human Microbiome Project, and the 1000 Genomes Project.

    Single Cell:

    Article Title: Genomic Sequencing from Sanger to Next-Generation Sequencing: Historical Context, Comparative Advances, and Prospects for Next-Generation Phenomics.
    Article Snippet: DNA sequencing has revolutionized biological and biomedical research, offering profound insights into genome organization, function, and variability.. From the pioneering Sanger capillary electrophoresis method to the advent of next-generation sequencing, the field has evolved toward unprecedented speed, scalability, and cost decreases over the years.. These advancements have enabled diverse applications across genomics, transcriptomics, metagenomics, epigenomics, and precision medicine, powering global initiatives such as the Human Genome Project, the Human Microbiome Project, and the 1000 Genomes Project.

    Article Title: Novel approach for diabetic wound healing: adipose-derived mesenchymal stromal cells Exo@SPHydrogel combined with laser therapy.
    Article Snippet: Subsequently, trypsin/EDTA (catalog number: 25200072, Gibco, USA) was used to incubate the tissues at 37°C for 5 minutes, preparing a single-cell suspension. .. Individual cells were captured using the C1 single-cell auto-preparation system (Fluidigm, Inc., South San Francisco, CA, USA). ..

    Article Title: Oligonucleotide encoded chemical libraries, related systems, devices, and methods for detecting, analyzing, quantifying, and testing biologics/genetics
    Article Snippet: .. Despite the rapid rise in high-throughput single-cell RNA-sequencing (RNA-seq) methods, including commercialized versions of automated platforms such as the Fluidigm C1, 10× Genomics or 1CellBiO systems, the application of single-cell RNA profiling for target agnostic high-throughput drug screening and target discovery is constrained by the lack of methods that can efficiently partition different drugs to different cells. ..

    Article Title: Mapping cellular heterogeneity and dynamic interactions in pancreatic cancer.
    Article Snippet: Pancreatic ductal adenocarcinoma (PDAC) is highly aggressive with a high mortality rate.. Intra-tumoral heterogeneity (ITH) increases the severity of PDAC and makes treatment difficult.. Insights are provided on ITH to understand the diversity of microenvironment (ME) components, biomarkers, different subsets of tumorassociated cells, and immune cells, as well as metabolic reprogramming, autophagy, and apoptosis in PDAC.

    RNA sequencing:

    Article Title: Oligonucleotide encoded chemical libraries, related systems, devices, and methods for detecting, analyzing, quantifying, and testing biologics/genetics
    Article Snippet: .. Despite the rapid rise in high-throughput single-cell RNA-sequencing (RNA-seq) methods, including commercialized versions of automated platforms such as the Fluidigm C1, 10× Genomics or 1CellBiO systems, the application of single-cell RNA profiling for target agnostic high-throughput drug screening and target discovery is constrained by the lack of methods that can efficiently partition different drugs to different cells. ..

    RNA Sequencing:

    Article Title: Oligonucleotide encoded chemical libraries, related systems, devices, and methods for detecting, analyzing, quantifying, and testing biologics/genetics
    Article Snippet: .. Despite the rapid rise in high-throughput single-cell RNA-sequencing (RNA-seq) methods, including commercialized versions of automated platforms such as the Fluidigm C1, 10× Genomics or 1CellBiO systems, the application of single-cell RNA profiling for target agnostic high-throughput drug screening and target discovery is constrained by the lack of methods that can efficiently partition different drugs to different cells. ..

    Drug discovery:

    Article Title: Oligonucleotide encoded chemical libraries, related systems, devices, and methods for detecting, analyzing, quantifying, and testing biologics/genetics
    Article Snippet: .. Despite the rapid rise in high-throughput single-cell RNA-sequencing (RNA-seq) methods, including commercialized versions of automated platforms such as the Fluidigm C1, 10× Genomics or 1CellBiO systems, the application of single-cell RNA profiling for target agnostic high-throughput drug screening and target discovery is constrained by the lack of methods that can efficiently partition different drugs to different cells. ..

    Lysis:

    Article Title: Mapping cellular heterogeneity and dynamic interactions in pancreatic cancer.
    Article Snippet: Pancreatic ductal adenocarcinoma (PDAC) is highly aggressive with a high mortality rate.. Intra-tumoral heterogeneity (ITH) increases the severity of PDAC and makes treatment difficult.. Insights are provided on ITH to understand the diversity of microenvironment (ME) components, biomarkers, different subsets of tumorassociated cells, and immune cells, as well as metabolic reprogramming, autophagy, and apoptosis in PDAC.



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    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 <t>microfluidic</t> (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.
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    Image Search Results


     Microfluidic-based  studies for biomolecular detection

    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

     Microfluidic-based  studies for drug screening

    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

    Cancer on a chip studies

    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

    Organ-on-a -chip studies

    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

    IsoLight single T-cell live functional immune proteomics profiling workflow.

    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

    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.

    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