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Cellsonics has redefined single cell sample preparation to capture the full potential of each and every tissue specimen. The innovative SimpleFlow™ tissue dissociation system enables researchers to capture important single cells in a user- and cell-friendly method.

Using the SimpleFlow™ system, any lab can obtain the full spectrum of heterogeneous cell populations from solid tissue samples in under 10 minutes while maintaining the native gene expression patterns across all cell types.


Enzyme-based tissue dissociation is cell- and workflow-unfriendly

Enzymatic tissue dissociation methods are cumbersome, inconsistent, and perturb cell surface markers and gene expression profiles1,2. Their disadvantages include:

Dedicated, trained technician required.

Dedicated, trained technician required.

Heated incubation associated with enzyme dissociation upregulates inflammatory and stress-related genes.

Cell viability and intact cell surface markers can be damaged during long complex workflows.

BLU™ energy technology ensures cell-friendly dissociation across specimen types

Sample preparation should not compromise biologically meaningful data. The SimpleFlow automated sample handling cartridge combined the proprietary Bulk Lateral Ultrasonic™ (BLU) energy enables researchers to focus on data generation, not sample preparation.

The SimpleFlow system automatically and gently dissociates fresh solid tissue specimens at cold temperatures using BLU technology generating suspensions of single cells without the need for enzymatic digestion. Following dissociation, cells are viable and ready for downstream applications, including FACS analysis, cell sorting for primary cell cultures and NGS studies.

Our simplified solution sets new standards for obtaining single cells from solid tissue with unparalleled speed, quality, and sample integrity. In addition, the novel BLU™ energy technology is integrated into a fluidic circuit in a single-use cartridge that provides a complete, automated dissociation workflow solution not possible using enzymatic methods.

Better cell population recovery with SimpleFlow

FACS analysis of dissociated mouse liver cells with the SimpleFlow System vs. enzymatic dissociation methods. Cell recovery of specific cell-types including CD45+ leukocytes (green boxes), and hepatocyte progenitor cells (blue boxes) was observed to be higher with the SimpleFlow system than with enzymatically-dissociated cells.

Healthy, unperturbed single cell recovery

Figure 2_Bulk RNA-Seq

Bulk RNA-Seq gene expression data generated from X tissue dissociated with the SimpleFlow system. Gene expression in SimpleFlow dissociated cells (blue lines) is closely aligned to that of the native non-dissociated tissue, whereas enzymatically-dissociated cells (green line) show genes that are both over- and under-expressed compared to the control tissue. Data have been normalized to the response from intact control tissue.

Bulk RNA-Seq gene expression profiles of genes associated with cell stress. Data from porcine liver tissue dissociated with the SimpleFlow system compared to enzymatically dissociated cells and intact whole tissue. Genes associated with cell stress signals are upregulated in enzymatically dissociated cells compared to control. Data have been normalized to the response from intact control tissue.

Figure 3_Bulk RNA-Seq_Stress Genes

Single cells from solid tissues in minutes

Unlike other tissue dissociation methods, the SimpleFlow system is easy to use and requires no technical training. The SimpleFlow system is designed to recover single cells in minutes in a hands-free workflow.

Typical Enzymatic Workflow

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Want more information?

Contact us to learn more about the SimpleFlow tissue dissociation system and/or set up a demo.


  1. Denisenko et al. (2020) Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biology 21:130.
  2. van den Brink, et al. (2017) Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat Methods 14, 935–936.