March 2018


New BD Accuri™ product manager Ben Johnson

Spotlight - Johnson

Ben Johnson accepted the role of global product manager for the BD Accuri™, BD FACSCelesta™ and BD FACSVerse™ product lines last summer. He explained how he went from being a BD Accuri customer to being its product manager and why he has a soft spot in his heart for cell cycle analysis.
Read the interview »


Join us at AACR and AAI

Noteworthy - Events!

We’ll be featuring the BD Accuri™ C6 Plus personal flow cytometer this spring at AACR 2018 (April 14–18 in Chicago, IL) and AAI 2018 (May 4–8, 2018 in Austin, TX). If you’re attending either event, we invite you to visit our booth where you can view a demo, talk to our scientists and ask our technical support experts questions.

Application Highlight

Use index sorting to correlate gene and protein expression at the single-cell level

Single-cell RNA sequencing (scRNA-Seq) can profile the transcriptome of every cell in a sample. However, this approach is not feasible when the cells of interest are rare in a heterogeneous sample. The use of fluorescence-activated cell sorting (FACS) upstream from genomic analysis can overcome this limitation by enabling you to isolate and interrogate only the cells of interest, devoid of unwanted contaminants, and to correlate protein and gene expression for individual cells.

Combining automation and ease of operation with high sensitivity and resolution, the BD FACSMelody™ cell sorter can sort and deposit single cells for downstream analysis. In addition, with a feature called index sorting, you can sort individual cells into each well of a plate while simultaneously recording (indexing) their fluorescence intensity in all channels. Later, you can correlate the indexed fluorescence intensity values with downstream data—such as RNA-Seq—for any cell you’ve sorted and analyzed.

App Highlight - Fig 1 - Thumb

Figure 1. Gating and single-cell deposition strategy

App Highlight - Fig 1 - Large
Figure 1. Gating and single-cell deposition strategy

Human peripheral blood mononuclear cells were isolated from a healthy donor and stained with antibodies. Cells were resuspended in BD FACS™ Pre-Sort Buffer and acquired on the BD FACSMelody. Lymphocyte singlets were identified using forward and side scatter. Viable CD3+ T cells were identified based on CD4, CD14, CD19 and 7-AAD exclusion, and CD8+ T-cells were identified and gated based on CD3 and CD8 expression (not shown). A. T-cell subsets were identified based on expression of CD197 (CCR7), CD45RO, CD183 (CXCR3) and CD95. Cells were categorized as TN, TSCM, TCM or TEM. B. Cells were sorted individually into 96-well plates according to the grid shown.

A new BD data sheet describes an experiment that pairs upstream index sorting with downstream RNA-Seq to perform a targeted gene expression analysis on different subsets of CD8+ T cells. The cells were acquired on the BD FACSMelody and immunophenotyped into increasingly differentiated CD8+ T-cell subsets—from naïve (TN) to stem cell memory (TSCM), central memory (TCM) and effector memory (TEM)—based on the expression of surface markers.

Using the gating strategy shown in Figure 1A, the cells were then sorted as single cells into 96-well BD Precise™ RNA quantification assay plates according to the scheme in Figure 1B. Each well of the plates contains specialized reagents that lyse the cell, extract the cellular RNA and enable preparation of a sequencing-ready library. A total of 368 cells were sorted individually into four plates for downstream gene expression analysis. The mRNA from these sorted cells was subjected to cDNA synthesis and library preparation using a targeted T-cell panel that contains primers for 220 different genes.

Using the index sorting feature of the BD FACSMelody, we then correlated phenotypic and gene expression signatures. Figure 2 shows partial results for a representative plate, from which we selected 26 TN, 26 TSCM, 20 TCM and 20 TEM cells in BD FACSChorus™ software. The software color-codes the selected cells in the plots for easy identification.

App Highlight - Fig 2 - Th

Figure 2. Correlation of protein levels with downstream mRNA signatures

App Highlight - Fig 2 - Large
Figure 2. Correlation of protein levels with downstream mRNA signatures

The index sorting feature of the BD FACSMelody enables researchers to select individual cells from the plate and correlate their data from downstream applications with their original cell phenotype—including markers that were not part of the sorting gate. A. Index sort data for a single 96-well plate showing individual CD8+ T cells within the gates used for cell sorting. B. Correlation of mRNA and protein levels of KLRB1 (CD161) in TEM cells. (MFI: median fluorescence intensity)

Figure 2A confirms that the sorted cells were located within the CD183 vs CD95 and CD197 vs CD45RO gates used to identify and sort them. Figure 2B plots protein expression levels vs mRNA levels for TEM cells and shows a moderate (R2 = 0.74) correlation between protein abundance and mRNA abundance. It is known that mRNA transcript levels only partially correlate with protein levels due to post-transcriptional regulatory mechanisms.1

Combining index sorting on the BD FACSMelody with single-cell RNA-Seq can provide deep insights into cell function and development. The ability to correlate protein and mRNA signatures on a single-cell level offers a rich opportunity for discovery. The BD FACSMelody can also improve assay efficiency, saving cost and time by enabling sequencing of only rare subsets of interest.

Genomic analysis is only one of many downstream applications of the BD FACSMelody across immunology, stem cell research, genomics, bioprocessing and cancer biology. With up to three lasers, nine fluorescence channels and one- or two-way sorting into plates, slides or tubes, the easy-to-learn, easy-to-use BD FACSMelody makes sorting accessible to more researchers and labs.

Download the new data sheet »
Download the BD FACSMelody brochure »


Tips & Tricks

Align peaks with VirtualGain™

In certain instances, a particular fluorescence peak should have the same position across different samples or be located at a specific channel number, regardless of staining. For example, to compare DNA and cell cycle distributions of different samples stained with propidium iodide (PI), their peaks should be aligned. With most flow cytometers, you must adjust voltage and amp gain controls to alter peak position from sample to sample.

Since BD Accuri™ cytometer voltages are not adjustable, there are no gain controls to adjust. Instead, you can use the VirtualGain™ tool in BD Accuri™ software to align the peaks. VirtualGain mimics voltage and amp gain adjustments to grossly reposition histogram data on the x-axis after data collection.

Accuri News - July - Figure 4 - Thumb

Figure 3. Use VirtualGain to align peaks of different samples

Accuri News - July - Figure 4 - Large
Figure 3. Use VirtualGain to align peaks of different samples

Chicken erythrocyte nuclei (CEN), calf thymocyte nuclei (CTN)—both from BD™ DNA QC Particles (Cat. No. 349523)— and Jurkat cells were prepared and stained with propidium iodide by standard methods. Data was collected on a BD Accuri™ C6 flow cytometer on different days. (Top row) In original data, DNA peaks from PI staining do not align. (Bottom row) VirtualGain was applied after data collection to align Peak 1 of the CEN and CTN distributions to Peak 1 of the Jurkat distribution. In row 4, overlays of DNA distributions show that after VirtualGain was applied, the peaks are aligned properly.

In Figure 3, three types of cells were stained with PI, but their peaks do not align sufficiently to compare their DNA distributions. In the right-hand column, VirtualGain was used to align the first peak of the CEN and CTN distributions with the first peak of the Jurkat distribution. The DNA distributions can now be compared.

VirtualGain is strictly an analysis tool and is not used while collecting data. It can be toggled on and off by clicking the asterisk under the parameter name. VirtualGain is applied only on histogram plots, and only on one parameter at a time. However, once VirtualGain is applied, you can view the transformed data in any type of plot. The tool affects only the displayed data and does not alter the raw data that is collected and saved in the FCS files.

For details on applying and using VirtualGain, see the BD Accuri™ C6 Plus System User’s Guide.


Publication Picks

This section highlights interesting recent articles that describe research using BD Accuri flow cytometers.

Immune dysfunction in space conditions

Van Walleghem M, Tabury K, Fernandez-Gonzalo R, et al. Gravity-related immunological changes in human whole blood cultured under simulated microgravity using an in vitro cytokine release assay. J Interferon Cytokine Res. 2017;37:531-541. PubMed

Autotrophic picoplankton

Tamm M, Laas P, Freiberg R, Nõges P, Nõges T. Parallel assessment of marine autotrophic picoplankton using flow cytometry and chemotaxonomy. Sci Total Environ. 2017;625:185-193. PubMed

Biodegradable nanoparticles for immunotherapy

Wilson DR, Sen R, Sunshine JC, Pardoll DM, Green JJ, Kim YJ. Biodegradable STING agonist nanoparticles for enhanced cancer immunotherapy. Nanomedicine. 2017;14:237-246. PubMed

Bacterial/blood-brain barrier interaction using iPSCs

Kim BJ, Bee OB, McDonagh MA, et al. Modeling group B streptococcus and blood-brain barrier interaction by using induced pluripotent stem cell-derived brain endothelial cells. mSphere. 2017;2:pii: e00398-17. PubMed



Meeting – April 14–18, 2018 – Chicago, IL
AACR 2018 (American Association for Cancer Research) »

Meeting – May 4–8, 2018 – Austin, TX
Immunology 2018 (American Association of Immunologists) »

1 Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13:227-32.

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