LatchBio

Curio Trekker and Curio Seeker kits represent some of the most advanced spatial omics technologies out there. We've been following their work for years. Now, Curio Biosciences just announced their new Bioinformatics Portal, powered by LatchBio! This is a huge milestone, months (years?) in the making, and we are looking forward to collaborate with them in helping researchers process and visualize large-scale spatial data more efficiently, ultimately making it faster and easier to draw meaningful conclusions. Curio and Latch will be sharing how these solutions work in practice during an upcoming webinar. If you’re interested in the possibilities of spatial omics and want a concrete example of how these tools can streamline your process, join below.

Single cell sequencing is one of biotech’s most powerful molecular microscopes. It gives scientists a window into the biochemical state of individual cells. We are well past a decade since this technique came online. As the technology continues to mature, and new kits that increase accessibility and throughput hit the market, the proportion of biological data generated from this modality will only increase. Analyzing this data is very difficult. It requires a tricky combination of interactive steps, large computing resources and some understanding of high dimensional data analysis to draw real biological conclusions. Bench scientists need better tools to independently explore and ask questions about single cell data. They have the most experimental context and an extensive understanding of the literature. Latch is developing a scientific plotting framework backed by large computers that allows biologists to interrogate their data with natural language. We have seen many classically trained molecular biologists complete end-to-end single cell analysis workflows on their own. We highlight the concrete steps with clear graphics here: 1/ Bringing in data and metadata from the cloud 2/ Subsampling counts by condition 3/ QC + filtering 4/ Normalization, dimensionality reduction + clustering 5/ Automatic cell typing 6/ Exploring cell types of interest, re-clustering + querying immunology literature These tools alone are not sufficient. They sometimes produce incorrect results and should be supplemented by computational teams with an understanding of the techniques. However, allowing scientists to play with their own data will allow for independent hypothesization, new biological questions and potentially new insights with material impact on drug programs. 

Spatial Biology West Coast 2024 should just be in San Diego every year . Actually , all conferences in general … my opinion! Great catching up with old friends and making new friends! Thanks LatchBio and Scale Biosciences for allowing Complete Genomics to co-host this event !

The past few years have brought an explosion in new commercial sequencing kits. They allow biotechs to measure molecular state with greater throughput complexity and accessibility. But these new kits generate a lot of data and analyzing it is becoming increasingly difficult. Kit providers need to carefully think through and manage this analysis for customers. Scientists will not purchase more kits unless they answer their biological questions. Here we walk through the best practices for the creation, distribution and troubleshooting of analysis packages for sequencing kit providers.

Chai-1 is available on Latch. This is a big model that predicts the properties and interactions of many types of molecules. It is competitive with AF3, RosettaFold, ESM3 on tasks that matter in discovery - eg. protein-ligand, PPI, protein-antibody. Strong technical work from the Chai Discovery team. Access at latch.bio.

How a hidden tab in an Excel spreadsheet wiped $12 billion off Amgen’s market cap. Nine months ago, Amgen published data from a phase 1 trial of their obesity drug prospect, MariTide, in Nature Metabolism. Unbeknownst to many, the Excel spreadsheets shared alongside the publication contained hidden tabs with crucial bone density data. An analyst at Cantor Fitzgerald discovered these hidden tabs, which included data on femur, lumbar, and hip DEXA bone density scans. One tab showed that patients on the highest dose of MariTide lost about 4% bone mineral density over 12 weeks, suggesting potential risks with GIPR antagonists like MariTide. Following the revelation, Amgen's stock plummeted 7%, wiping out $12 billion in market cap. Jefferies analyst Michael Yee called the bone density concerns a "non-issue," noting the data was inconsistent and based on a small sample size. Amgen stated they saw no association between MariTide and bone density changes and remained confident in their drug... In scientific research, transparency and clarity is crucial. Excel's lack of traceability and the ease of missing data can lead to significant misunderstandings and massive financial repercussions. Amgen anticipates the phase 2 topline data for MariTide later this year, I imagine they won't be using the hidden tabs feature this time around.

Dyno Therapeutics uses machine learning guided engineering of large molecular libraries to build new AAVs with: - 100x greater brain transduction - 10x improved liver detargeting - comparable production Inspired by their work, Hannah Han L. replicated a historic paper by Ogden et al, which took a look at all single-codon mutants of the AAV2 cap gene across 735 positions. She reproduced some of the key scientific results and had fun thinking about the same biological questions the Dyno team was confronted with years ago: 1/ How do mutations effect viral production? 2/ How do mutations effect biodistribution? 3/ How can we use machine learning to generate new, optimal designs? Along the way she digs into the guts of interesting visualizations and shows how to use Latch Plots — a Python-based scientific plotting framework - to answer these concrete questions.

A very special night in San Francisco bringing together the biotech community for the first stop on the Founder-Led Biotech Tour. A huge thank you to our SF co-hosts - Kevin Parker, Seemay Chou, Josh Moser, David Schaffer, Brandon Wilson, Ph.D., Alfredo Andere 🦖, Ashley Zehnder, Lexi Rovner, John Suliman, Armand Cognetta, Nathaniel Chu, Lada Nuzhna, Raphael Townshend, Eerik Kaseniit, Brandon White, Nicholas Larus-Stone, Nicolas Tilmans, Egan Peltan, Michael Becich, Adil Yusuf, Richard Yu, Tim Schnabel, Tess Bevers, Jacob Borrajo, Ashton Trotman-Grant, Ivana Muncie-Vasic, Janine Sengstack,Trevor Martin and Jasmin Hume, PhD. Thank you to Future House for hosting and for our wonderful sponsors for making this all possible. Global Sponsors: J.P. Morgan, Wilson Sonsini Goodrich & Rosati, Eli Lilly and Company. City Sponsor: LatchBio Founder-Led Biotech upcoming stops: NYC 11/14 London 11/18 Boston 11/20 Register here: https://founderledbio.com/

Many legitimate therapeutics companies said "no" to Latch early on. These are emails I received in May of 2022 from lost clients. We have since listened intently to their feedback, and built what I think is a genuine solution to a huge problem in biotech. It is called Latch Plots. 📊 📈 -- For context, in 2022, our product was based on interviews with 100+ bioinformatics and computational teams in biotech. We knew data & computing infrastructure for these teams was important, but missed a major problem the broader biotech organization faces: scientific data interpretation. When a bench scientist (a bioengineer, chemist, molecular biologist, geneticist, or immunologist) receives data from a computational team (a data engineer, data scientist, or bioinformatician), they have questions. A ton. "What is the meaning of this data? How does it work? Can I tweak the plots? What other samples can I compare here? What are the axes? What do these genes do? How can I best visualize what's going on? Can we re-run this with different parameters?" Typically the plots are hard to change, so developers hear these questions, go back, change the code, tweak the statistics, and work on making new plots. Meanwhile the scientists (who really want to do their own analysis) are just waiting... This creates a huge bottleneck and slowdown in the scientific method of observation, hypothesis, and conclusion. Scientists want to conclude independently, in minutes. Not wait for you to write code. If developers want to iterate on code, and wet lab scientists want to iterate on plots, you inevitably get teams working in siloed and disjointed systems, doing their own thing in a way that is slow and impossible to track. We noticed that the best teams in biotech have fast, iterative feedback loops between wet <> dry lab. These teams focus on offering scientists self-serve insights with statistical integrity. Scientists are then empowered to answer questions on their own. It turns out this is super valuable - just really hard to do with existing tools (Graphpad Prism, Jupyter, RShiny, Plotly, etc.). This is the problem our customers wanted to solve in 2022... Hence the emails. That is why I'm so psyched about the launch of Plots. And incredibly proud of the latch engineering team for what they shipped. Latch Plots is a hybrid graphing software for programmers and non-programmers in biotech to generate scientific conclusions, together. Developers can write code in cloud-hosted notebooks, configuring flexible visualizations for scientists. Anything can be created, from differential expression, pathway enrichment, and single-cell clustering and annotation, to large scale statistical GWAS studies. I'm excited to see how people will push the boundaries with this tool. And hopeful this improves the communication and collaboration between scientists and developers. If it doesn't, let us know - we'll listen to your feedback.

Huge shout out to the eng + product team at Latch, they have been shipping like crazy! Here is a recap of just the past month: 1) Assembled a Protein Engineering Toolkit with 16 user-friendly pipelines. Tools like RFdiffusion let you design new protein structures. We did a case study building plastic-degrading enzymes and cholesterol drugs. 2) Launched Latch Plots—a new way to visualize biological data. Traditional tools like graphpad weren't cutting it, so we built something ourselves. It's no-code + python-based with an AI integration, so you can generate plots just by describing what you need. 3) Used GPUs to speed up single-cell data analysis. Tasks that used to take over 30 minutes now run in under a minute. It's pretty game-changer for handling large datasets within a notebook. 4) Also on the GPU front, an accelerated version of nf-core/methylseq for epigenetic analysis. With data sizes exploding (1M+ cells), using GPUs in bioinformatics workflows is becoming essential, and we're seeing significant performance boosts. 5) The second biotech data infrastructure conference where engineers and scientists shared ideas openly. It was amazing to see so much collaboration. We're already planning to do it again next year. And they are already iterating on the next set of shipments with users, looking forward to continue watching their pace.