David McKellar

@GorinGennady Reminds me of one of my favorite recent single-cell tech papers from @AdamCribbs - check out Fig 1 - nature.com/articles/s4200…

Feature barcoding primers! Somehow 👀 the primers are missing their UMIs and antibody capture sequences. The result: vast numbers of reads with TSO/primer/FB cell barcode/poly(A). If the barcode/poly(A) is close enough to a real transcript, it gets counted, giving outliers. 8/

The empty drops you threw out in your single-cell RNA sequencing analysis might be hiding mysterious things 👀 check out our bioRxiv preprint! The empty drops do not contain cells. Yet we can still use them to learn interesting things about biology and technology. 1/

Spatial transcriptomics needs to look beyond just mRNAs. Great to see this work from @ntekasi, Lena Takayasu, @IwijnDeVlaminck, and me in a past life out today!

@kenbwork 200+ known chemical modifications, 1000s of new transcripts/isoforms added to gencode annually- mRNA tools may be maturing, but tools to actually profile RNA are in early days

Transcriptomics is very useful but overdiscussed because measurement tools are becoming mature. No one is actually ready for the complexity of proteomics. Can be thousands of proteoforms for a single protein, all with identical mass, near identical fragmentation patterns, very low abundance. Crazy numbers here: 10^6-10^8 *types* per cell and ~10^10 total counts. Most current tools (eg. many MS flavors) work with bulk samples and even then cannot pick out subtle chemical differences, blending distinct molecules and obfuscating potentially important biochemistry.

Congrats to RNA-multiomics evangelist @ntekasi on defending your PhD today! Loved that this slide happened to be # 42 in the deck!

Looking forward to catching up with folks at the NYC RNA Symposium w/ @Tri_I_RNA tomorrow!

It's not "direct" if you have to run RT/RCA..

@shelbynewsad Text-to-enzyme would completely flip how r&d is done. Going to start looking away prompts for when someone builds this...

(New thesis) AI-ification of R&D proteins There’s been an emergence of new papers on computational antibody, peptide, nanobody, and and enzyme design. While these are all relevant for new therapeutics, there’s massive market opportunity in R&D reagents. (This thesis was started by a conversation with @an1lam.) Research and development in companies and academia stems from our exploitation and synthesis of new proteins (namely, antibodies and enzymes). From staining tumor samples, to copying DNA with polymerases, to cutting DNA for cloning, proteins are workhorses of biotech. Not only this, but proteins are the commodity most likely to spoil because of their propensity to denature at room temperatures and through freeze/thaw cycles. Not only this but the and cost to make and purify means proteins are likely the most expensive reagent in experiments. If you’ve ever worked in a research lab you know that you always need to keep the antibodies and enzymes chilled because they cost hundreds to thousands of dollars and are the first failure point of experiments. Given the proliferation of antibody and enzymes design papers, it’s logical to apply the gains in those methods to R&D proteins. Namely, thermostability, miniaturization of antibodies for cheaper manufacturing, higher fidelity signals or specificity, all of which can be improved with the right assay <> AI loop. Not only this but @plasmidsaurus teaches us that good user experience and better products can lead to massive customer pull and rapid uptake. This matters commercially because these companies garner therapeutics-value acquisitions and market caps. While these have historically taken decades to reach prominence, the decreased cost and speed of development of new proteins could change the incumbent dynamics. - $5.25 billion acquisition of antibody and reagent maker, Biolegend by PerkinElmer - $5.7 billion acquisition of research antibody company Abcam by Danaher Bio-Rad Lab market cap is $7.3 billion - Smaller acquisition of companies such as Novus Biologicals by Bio-techne for - $60 million show a skew of outcomes possible Other thoughts: - Team will have to thread the needle of highest margin and lowest technical difficulty - Team likely won’t have to build their own models initially but the wet lab <> ML feedback loop will be crucial in rapid optimizations - Companies here could expand margins by layering biomanufacturing unlocks to produce and make R&D proteins at lower cost, in less time Check out the thesis below on the @CompoundVC Thesis Database -

Pre-Seed should not take months and months to raise! We are thrilled to announce that @2048vc is launching Pre-Seed Fast Track to help you get funding in 10 business days. If you are raising $500K - $1.5M Pre-Seed round in Vertical AI, Deep Tech, Health or Bio we would love to meet you! We offer clear and transparent process and money in the bank within 10 business days of the first meeting. To start the process submit your pitch through our short form - 2048.vc/fasttrack_pitch If there is a potential fit we will reach out within a few days and follow these steps to fund you: Step 1: Intro meeting via Zoom: 30 mins where you meet with 1 member of our team. Step 2: Deep Dive via Zoom: 60 mins where you meet with 3-4 members of our team. Step 3: References + In-Person Meeting: We will chat with your early customers and professional references, and will spend a few hours to get to know each other in person. Startups are long journeys and we know how important the investor-founder fit is, so we will gladly pay for your travel. Step 4: Term sheet: We make you an offer from $250K - $750K Step 5: Wire: We are high conviction and don’t wait for the rest of the round to come together - we wire as soon as we sign the docs. To learn more about Pre-Seed Fast Track visit 2048.vc/fasttrack. We can’t wait to review your pitches and to meet you! @2048vc // #preseed

@Dr_ASingh @MEGeorgiaTech Go Jackets!

A $100M transformative gift! Grateful to John Durstine (1957 @MEGeorgiaTech graduate) for his extraordinary generosity and vision. His support will have a transformative impact, and his commitment to advancing Georgia Tech Mechanical Engineering faculty and students is inspiring!

Saying a bittersweet goodbye to @nygenome today. Have had an awesome few years working on spatial tech with @mssanjavickovic and the rest of @NYGCtech! Excited to build some new RNA tech with old friends!

Beyond polyA: scalable single-cell total RNA-seq unifies coding and non-coding transcriptomics biorxiv.org/content/10.110… #biorxiv_genomic

NYC has the talent. Now it needs the infrastructure. To become the true capital of AI, we must build our own models, share data, and invest in massive compute power. My thoughts on how NYC can move from AI adopter to AI inventor profelemento.substack.com/p/what-will-it…

@kenbwork Shout out @mantri_madhav and his beautiful data!

Spatial Segmentation and Image Alignment

Spatial biology is the most powerful + data intensive tool in biotech history but ~1TB outputs and fragmented tools make analysis difficult. We built a H5 viewer using AnnData, PMTiles + DuckDB that renders millions of cells, transcripts and tiled high content images in the browser.

Thanks to everyone who came out to our RNA x Tech Dev Symposium at @nygenome! The selfie is only blurry because I was so excited about the fantastic talks from @mason_lab, @IGainetdinov, @wu_xuebing, and @mssanjavickovic!

Applications are NOW OPEN for the RNAConnect 5K Sprint Award! Compete for a $5,000 cash prize to support your innovative RNA research. 🔗Learn more: bit.ly/3H80DWD #RNA #RNAResearch #ScienceAward #ScienceFunding #ResearchGrants

Noticed that noncoding RNAs come before mRNAs in the "RNA" entry on wikipedia and that made my day- en.wikipedia.org/wiki/RNA

This is great work! I have been ranting to labmates about bar odes for 2 years- now I can just send them your paper!

Still time to sign up for this event next week! So far, across the ~120 registrants we have 7 academic institutions, 4 companies, and 2 journals represented!

@sinabooeshaghi Unmapped too!

biologists with unpaired sequencing reads: