ASHG 2022

Thoughts from day 1 of ASHG 2022.

ASHG 2022 at the LA Convention Center

Day one was mostly about coordinating the commute to LACC and attending industry talks in the exhibit hall. Although I drove up and parked in the Expo/Sepulveda metro station parking before riding the E line to LACC, I realized later I can take the CC6 bus that I normally take to UCLA straight to the station in the future. In the future I’ll have to try taking the connection from the 7th street station to Long beach. Short notes on the talks I attended:

Golden Helix Colab

These were not actually Google Colab workshop sessions. “Colabs” are the name they gave to 30 minute industry talks given at the edges of the exhibit hall. First up was the Golden Helix talk presented by CEO Andreas Scherer, covering the latest for Varseq as well as general tips for scaling up as a testing lab. Exomes, as expected, are their bread and butter and what they recommend building out from. A portion of the talk was spent on how they prepared a custom solution for Denmark’s national genome center. Institutions at that scale have their data airgapped, so they had to send the updates to VarSeq annotations through a DMZ. An upcoming development in Varseq is TSO-500 support - it would be great if this means they have the groundwork for building out solutions for other pan-cancer or multi-cancer panels.

Metabolon Colab

This was a primer on metabolomics from one of the leaders in the field, Metabolon. They didn’t mention much I didn’t already know despite my very limited knowledge in this area. Case studies included blood matrix metabolomics indicating an association between hexadecanedioate and hypertension, as well as mQTLs associated with kidney disease identified through urine (and notably are not concordant with blood, indicating unique insight from urine).

Promoter and enhancers (Vertgen lab)

HAQER enhancer regions (must be a coined term, hard to Google) changed relatively quickly across hominid evolution, and are associated with long range chromatin interactions. Trans-chromosome interactions were identified using STARR-seq. They also performed a meta-analysis on Hi-C datasets creating a meta-Hi-C aggregate dataset for use in identifying eQTLs. Coexpression was used to validate the eQTLs under the assumption that colocalization (identfied by Hi-C) would be associated with coexpression. The Gillis lab has their resulting Hi-C contact network available for download and use online.

IDT Colab

IDT had a Colab session describing the basics of MRD detection and their offerings for colorectal and lung cancers. I had read a little on this area before so there was not much new here to discuss that’s not already in my cfDNA notes and paper review.

Parse Colab

Evercode WT2 (whole transcriptome) technology is their updated single-cell WT offering, but what was interesting is that they use a fundamentally different method for single cell sequencing. Rather than droplet based methods, they use “split pool sequencing” where the single cell suspensions are split across 96 well plate with well-specific barcodes across 4 passes of splitting, barcoding, and re-pooling before splitting again. The result is that each cell has a unique path through the barcode split pooling process and can be identified through its unique set of barcodes. Note that 4 rounds of 96 well splits is 96^4 or ~1M unique barcodes. So you can have that many cells across up to 96 sample. Also they noted that the first round of barcoding is also the sample barcode, so you don’t need to sample barcode separately. My impression is that this is a very smart method for single cell, and they showed lower doublet rates compared to droplet methods (they attribute this to avoiding the well known problem of ambient RNA in droplet methods, referencing a Caglayan et al paper). I had also never heard of Singulator, which is the instrument used for single cell suspensions.