- Sony Biotechnology
- Sony Biotechnology
Gene expression profiling is a powerful approach that promotes a deeper understanding of the characteristics of cells at the transcriptional level and has broad implications for basic and clinical research. Historically, gene expression profiling has been performed on populations of cells, typically cell lysates, where observed expression levels represent an average of the unique expression states of each cell within the population. Such phenotypically similar populations can be purified in bulk by flow cytometry based cell sorting prior to RNA analysis. 1,2
Placing several fluorescent proteins together in a flow cytometry panel offers greater power and capability for experiments. However, handling autofluorescent signal with fluorescent proteins is out of reach for conventional flow cytometetry users. Sony spectral flow cytometry analyzers enable researchers to harness up to five near infra-red fluorescent proteins in a single experiment. Moreover, spectral technology lets users accurately identify autofluorescence, and eliminate it if needed.
Bacteria such as E. coli are popular model systems for engineering and production of modified proteins. Yet the idea of putting bacteria in a cell sorter conjures unwelcome images. Take heart, the Sony SH800 cell sorter, simplifies decontamination by quickly and easily letting researchers replace key components that come in contact with the sample. Here are some examples, and publication citations.
Most commercially available antibodies contain small amounts of preservatives such as sodium azide to prevent microbial growth. However, sodium azide is also toxic to mammalian cells as it inhibits cellular respiration. Actual toxicity varies by cell type with neuronal cells being most sensitive. Toxicity is concentration, time, and temperature dependent. For most cell sorting experiments the health of cells are not impacted because the antibody is diluted and cells are typically incubated on ice for less than one hour.
Join us for a free webinar on Spectral Flow and FCS Express 6 which provides native support for Sony spectral data files. See how spectral flow cytometry delivers better data and simplifies panel design. In addition we’ll show how seamless integration between FCS Express and Sony spectral flow cytometry analyzers allows you to move quickly from acquisition to expanded data visualization with spectral overlays, tSNE, Spade, and plate based heat maps.
Flow cytometry has long been considered a tool of hematologists and immunologists who primarily work with the hematopoetic system. Flow cytometry is capable of performing the simultaneous detection of more than 20 parameters; however it requires the sample to be prepared into a single cell suspension. The need for a single cell suspension makes the sample preparation more intensive for cells derived from tissues. Many tissues are also highly autofluorescent, which results in background noise that limits detection of low levels of expression on conventional flow cytometers.
Listen to Dr. Feng Zhang who was first to adapt CRISPR-Cas9 for genome editing on eukaryotic cells explain CRISPR:
Pluripotent stem cells (PSCs) offer an unprecedented opportunity for both disease modeling and personalized medicine. In particular, PSC derived cardiomyocytes (CM) mature into adult cardiomyocytes when transplanted into neonatal rat hearts allowing iPSC modeling of cardiomyopathy. A recent published study by Kwong et al 1 shows the successful isolation of cardiac progenitor cells (CPCs) from mouse embryonic stem cells. To achieve this, a mESC line expressing Isl1-Cre; Rosa-RFP; aMHC-GFP was generated. The expression of Rosa-RFP allowed tracking of CPCs destined to mature into CM. RFP+ CPCs were sorted using the Sony SH800 cell sorter using the 130um microfluidics sorting chip.
Immunotherapy has shown great promise as a cancer treatment resulting in several FDA approved drugs and clinical trials. For example, a promising therapy in several tumors is the blocking of the PD-1/PD-L1 interaction that is over expressed on many types of tumor cells. However, success depends on the patient’s immune system to respond to the tumor.
As most researchers know, all instruments have some variability and over time settings continue to drift. Obviously, instrument variability is “bad” because it can unwittingly impact your data – forcing you to re-run experiments to determine what caused data anomalies.