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
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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

  • 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
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inDrop Workflow

Step 1

Isolate and suspend cells
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Step 2

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

Step 3

Build sequencing library
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(Sequence)

Step 2

Apply informatics pipeline
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(Analyze single-cell transcriptional data)

Science

Publications

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.


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

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

Bioinformatics

Download the ESAT code [GitHub]
See the Publications section for the original paper.

How to Order

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