Lessons From the Recent HCA 2022 General Meeting: Applying Single-Cell Data to Clinical Decision Making

September 23, 2022

Contributed Commentary by Dr. Zach Pitluk, Paradigm4 

September 23, 2022 | The recent Human Cell Atlas (HCA) 2022 General Meeting, hosted in Vienna, Austria, provided a great opportunity for me to catch up on progress towards the HCA’s goal of mapping every cell type in the human body. I also got the chance to hear presentations that either highlighted new insights gleaned from HCA data or described the outcomes from ongoing collaborations between research groups. 

In particular, I wanted to hear from innovators making the first translation of single-cell data into clinical situations. Since mid-2021, many papers have described actionable links between genome-wide association studies and specific diseases based on single-cell data. 

The presentation by Alexander van Oudenaarden at the Hubrecht Institute on “Integrating sc-riboRNAseq with scRNA-seq measures changes in translational efficiency” looked at where the rubber meets the road: consequences of mutations on proteins. Alexander’s lab demonstrated that synonymous mutations could cause pauses in translation that can be detected using sc-Riboseq. They also looked at the cell cycle distribution of translation and transcription. Using the tools and approaches from the Hubrecht Institute should enable a deeper understanding of the biological consequences of 5’UTRs and synonymous mutations, especially synthetic interactions between proteins in complexes mediated through translational pausing. 

The potential power of single-cell data is becoming more apparent with each study. But one associated challenge that comes up repeatedly is the handling, storing, and analyzing of the abundant data that is now available. There is a strong consensus that these data-related challenges will continue to grow with the increasing application of multi-omics. I believe that single-cell science is far from what we might call “settled science.” The HCA meeting confirmed the dynamic nature of the field and the need for more efficient computation and data management. 

For example, just a few months ago, the hot topic was a discussion around the validity of dimensionality reduction. The workflow utilized to create and process single-cell RNA sequencing (scRNA-seq) data includes algorithms that generate a reduced dataset representation while still aiming to maintain the integrity of the original data. A vector of 10,000 genes is reduced to two or three dimensions to cluster cells with similar transcriptional profiles and enable visual representations of the data to be constructed. Everyone agrees that dimensionality reduction should filter noise and identify relevant connections in large-scale datasets. In many of the presentations and discussions I had throughout the meeting, it is clear that clustering is a required first step in the workflow. In Aviv Regev’s words, “cell programs become the focus of further analysis.” In the same way that genome sequencing forced a re-appraisal of Linnean species definitions, scRNA-seq and its sister methods are quickly changing our understanding of cell types in the body. 

Interestingly, this change shifts the focus to downstream data processing, highlighting issues in many areas: data wrangling, consistency, ability to query across studies and datasets with ad-hoc analysis, scalability of analytical tools, ease of use for the research team, and the cost of computing with large datasets. Each of these topics requires an article of its own to explore and consider the differences between approaches, but you won’t be surprised to hear that, in my view, this is also far from settled science! 

The HCA meeting highlighted the need for a more integrative and extensible analytics platform to apply single-cell data in richer cell atlases to clinical situations. Delivering these capabilities along with the additional requirements for a regulatory compliant, easy-to-use approach at a significantly lower and more predictable cloud spend will allow pharma/biopharma—and clinical researchers/physicians—to make decisions in a more timely and cost-efficient way. 

Dr. Zachary Pitluk is Vice President of Life Sciences and Healthcare at Paradigm4. He has worked in sales and marketing for 23 years, from being a pharmaceutical representative for BMS to management roles in Life Science technology companies. Since 2003, his positions have included VP of Business Development at Gene Network Sciences and Chief Commercial Officer at Proveris Scientific. In addition, Zach has held academic positions such as Associate Research Scientist, Postdoctoral Fellow, and Graduate Student at Yale University Department of Molecular Biophysics and Biochemistry and was named as co-inventor on numerous patents. He can be reached at zpitluk@paradigm4.com.