Single Cell RNA Sequencing Analysis Platform

inDrop™ System

The inDrop ™ System is the only scRNA-Seq platform that provides enhanced experimental control, more actionable information and a lower overall cost per result compared to other existing platforms.

Scientists can now conduct more studies looking at more cells to gain more insights.

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inDrop Highlights

Less expensive to run more samples

Flexibility with design experiments

Ability to process challenging samples

Maximize data from clinical samples

System Advantages

Highest encapsulation rates (>90%) with low doublets
Control droplet size to encapsulate single nuclei, yeast spheroplasts, protoplasts, or even embryos
Change reagents, library prep protocols or flow rates to fit your application
Lowest cost per sample or cell
Customizable gel beads

Diverse Applications

Cancer: Tumor profiling
Immunobiology: B-cell and T-cell receptor analysis
Developmental Biology: Cell-to-cell variation identification
New Application Development: scATAC-seq, nonPolyA, scNuclei, Targeted scRNA-seq
Large Cell Types: Cardiomyocytes, Megakaryocytes, Neurons

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inDrop Workflow

Step 1

Isolate and suspend cells

Step 2

Hydrogel Bead-Cell co-encapsulation

(In-drop synthesis of barcoded cDNA)

Step 3

Build sequencing library

(Sequence)

Step 2

Apply informatics pipeline

(Analyze single-cell transcriptional data)

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Science

Publications

A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte. Plasschaert L. W. et al. Nature 560 377-381 (2018).

Population snapshots predict early haematopoietic and erythroid hierarchies. Tusi B. K. et al. Nature 555 54-60 (2018).

Single-Cell Analysis of Experience-Dependent Transcriptomic States in Mouse Visual Cortex. Hrvatin S. et al. Nat Neurosci. 21 120-129 (2018).

Clonal analysis of lineage fate in native hematopoiesis. Rodriguez-Fraticelli A. E. et al. Nature 553 212-216 (2018).

Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. Azizi, E. et al. Cell 174 1-16 (2018).

Single-cell gene expression reveals a landscape of regulatory T cell phenotypes shaped by the TCR. Zemmour, D. et al. Nature Immunology 19 291-301 (2018).

Single-cell transcriptomics of the developing lateral geniculate nucleus reveals insights into circuit assembly and refinement. Kalish B. T. et al. PNAS online E1051-E1060 (2018).

Unsupervised trajectory analysis of single-cell RNA-seq and imaging data reveals alternate tuft cell origins in the gut. Herring C. A. et al. Cell Syst. 6 37-51 (2018).

Single-Cell Transcriptomics of a Human Kidney Allograft Biopsy Specimen Defines a Diverse Inflammatory Response. Wu H. et al. J Am Soc Nephrol. 29 2069-2080 (2018).

The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Briggs J. A. et al. Science 360(6392) (2018).

Haematopoietic stem and progenitor cells from human pluripotent stem cells. Sugimura R. et al. Nature 545 432-438 (2017).

Dissecting hematopoietic and renal cell heterogeneity in adult zebrafish at single cell resolution using RNA sequencing. Tang Q. et al. J. Exp. Med. 214 2875-2887 (2017).

Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. Schelker M. et al. Nat. Comm. 8 8-12 (2017).

Comparative analysis of kidney organoid and adult human kidney single cell and single nucleus transcriptomes. Wu H. et al. bioRxiv doi: http://dx.doi.org/10.1101/232561. (2017).

Mouse embryonic stem cells can differentiate via multiple paths to the same state. Briggs A. A. et al. eLife 6 1-23 (2017).

Single-cell barcoding and sequencing using droplet microfluidics. Zilionis R. et al. Nature Protocols 12 44-73 (2017)

A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. [Epub ahead of print] PMID: 27667365
Baron M, Veres A, Wolock SL, Faust AL, Gaujoux R, Vetere A, Ryu JH, Wagner BK, Shen-Orr SS, Klein AM, Melton DA, Yanai I.;
Cell Syst. 2016 Sep 21. pii: S2405-4712(16)30266-6. doi:
10.1016/j.cels.2016.08.011.

End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data.
Derr et al.
2016, Genome Research 26(10): 1397-1410. Epub 2016 Jul 28

Droplet Barcoding For Single-Cell Transcriptomics Applied To Embryonic Stem Cells
Klein et al.
2015, Cell 161, 1187-1201

Marrying Microfluidics and Barcoding Technology
Cell, May 21, 2015, 161, Issue 5