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.

Request a Quote See Workflow
Image

inDrop Highlights


Less expensive to run more samples


Flexibility with design experiments


Ability to process challenging samples


Maximize data from clinical samples

System Advantages

  • The Industry's Highest Encapsulation Rates: >90% with low doublet
  • More Actionable Info: Rare cell subpopulations and low abundant, bias-free transcripts
  • Versatile Input Requirements: Diverse cell types, sizes and quantities
  • Low Overall Cost Per Result: As low as 5¢ per cell

Diverse Applications

  • Cancer: Tumor profiling
  • Immunobiology: B-cell and T-cell receptor analysis
  • Drug Discovery: Identification and validation of new drug targets
  • Stem Cell: Cell-to-cell variation identification
  • Developmental Biology: Cell lineage tracing
Image

inDrop Workflow

Step 1

Isolate and suspend cells
Image

Step 2

Hydrogel Bead-Cell co-encapsulation
Image
(In-drop synthesis of barcoded cDNA)

Step 3

Build sequencing library
Image
(Sequence)

Step 2

Apply informatics pipeline
Image
(Analyze single-cell transcriptional data)

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

Bioinformatics

How to Order

Get in touch our team and we will be happy to put a quote together.