for those who might have missed it, the links discussed things such as
It is hard to understate just how big a deal it would be if we could edit thousands of genes in cells all over the human body. Nearly every trait and every disease you’ve ever heard of has a significant genetic component, from intelligence to breast cancer. And the amount of variance already present in the human gene pool is stunning. For many traits, we could take someone from the 3rd percentile to the 97th percentile by editing just 20% of the genes involved in determining that trait.
Tweaking a few hundred genes might be able to halt the progression of Alzheimer’s, or cure untreatable depression.
The same could apply to other major causes of aging: diabetes, heart disease, cancers. All have genetic roots to some degree or other. And all of this could potentially be done in people who have already been born.
And
Based on the model, we can come to a surprising conclusion: there is enough genetic variance in the human population to create a genome with a predicted IQ of about 900. I don’t expect such an IQ to actually result from flipping all IQ-decreasing alleles to their IQ-increasing variants for the same reason I don’t expect to reach the moon by climbing a very tall ladder; at some point, the simple linear model will break down. But we have strong evidence that such models function quite well within the current human range, and likely somewhat beyond it. So we should actually be able to genetically engineer people with greater cognitive abilities than anyone who’s ever lived, and do so without necessarily making any great trade-offs. [and the article focuses on the potential of doing these types of things in humans who are adult today]
You may already be doing single cell RNA sequencing to study the transcriptome but did you know that there are over 3,000 proteins that are predicted to be secreted and are currently understudied? These include antibodies, cytokines, growth factors, proteases, and extracellular vesicles that are often washed away before scRNA-seq. Moreover, the first step in scRNA-seq is to lyse or fix the cell, making it impossible to ask these cells additional questions. With Nanovial technology you can isolate single cells along with their secretions, retaining that paired information, while keeping the cells alive. This lets you:
Screen your cells based on their secretion levels and find new populations with distinct secretory profiles.
Find antigen specific antibodies for rare disease targets.
Recover cells for a variety of downstream assays, including getting paired secretion and transcriptome information.
Use the instruments you already have for assay readouts.
I’ve put together a few highlights below to help you learn more about the technology and how our current users are leveraging single cell secretion information.
Don’t hesitate to reach out if you have additional questions or if you’d like to schedule a call with our R&D scientists to talk about your project more in depth.
Kind regards,
Zuly Peralta
Sr. Product Manager
Screen Cells Based on Their Secretions at the Single-Cell Level!
Watch this 5 minute video to learn how Nanovials work and how they can be used to further your research.
Publication Highlight: Optimize Cell Therapies by Profiling Extracellular Vesicle Secretion
Researchers sorted stem cells based on the level of secreted vesicles, and used these on a mouse model of myocardial infarction. Learn how they got more insights into cell to cell communication and how imaging cytometry confirms measurements from Nanovial Assays. Read the publication
Publication Highlight: Uncovering Novel TCRs for Viral and Cancer Antigens
Learn how researchers at UCLA used Nanovials to identify T cells that respond to viruses and rare prostate cancer targets. The discovery of three new receptors for prostate cancer opens up avenues for new immunotherapies. Read the publication.
Make the most out of your sample and get paired secretion and transcriptomic data with a SEC-seq workflow. Find gene expression profiles driving secretion and discover new biomarkers for unique cell types. View the Application Highlight
