Doloromics

We know that nociceptors interact with tissues - they innervate - in different ways. What we can actually do is look at the signals communicated between the tissues of innervation. And identify how those signals and mechanisms are changing in different types of chronic pain. Our aim is to develop disease-modifying therapeutics to eliminate chronic pain.”

[The sequencing data is] not just from healthy individuals, but individuals with varying different forms of neuropathic pain.

The tissue is difficult to work with due to a number of factors, such as cell morphology. So my responsibility was establishing the scientific workflows and analyzing the sequencing runs from various different sequencing techniques we were piloting.

From there, we started to build out that Atlas itself. Now, we’ve taken that data and we have used it to inform the generation of a stem cell line that we differentiate into sensory neurons that allows us to validate the therapeutic targets that we found.

So we essentially go from human tissue and identifying targets from patients with chronic pain, to recapitulating the disease microenvironment within a cell culture, to be able to then test therapeutics that we’re developing and validate therapeutic targets.

As you can imagine, this requires a significant bit of single-cell sequencing to plot differentiation of cells at different time points. We need to ensure the environment we are testing in accurately recapitulates the human tissue.”

Here we are with tons and tons and tons of data... From the beginning, I wanted data to be accessible to everyone in the organization. I played around with Galaxy, s3 buckets, and many options. We considered on-site servers.

I knew this was unsustainable. As a co-founder of a startup, we have choices to make as it pertains to hiring - do we hire an incredibly talented bioinformatician or an incredibly talented scientist in pain research? And what if we could combine those?

Whoever we hired, I wanted everyone to access the data. I really wanted to level that playing field. Coming from neuroscience, I didn’t understand why there is such a walled garden around bioinformatics.

Increasing access to that walled garden by making tools just a bit more accessible - this is what drew me to Latch. I wanted us to be able to grow: to hire scientists, bioinformaticians, anybody, without them worrying about a skillset deficit.”

Onboarding our lead scientist onto Latch and getting her acquainted with our data was painless. It allowed her more time to get familiar with the data we had generated. This was huge. And this really leveled that playing field.

Now, if I hire a bioinformatician tomorrow, Latch enables them to work with their own workflows in a familiar, custom environment. And if I went and hired 5 wet lab scientists tomorrow, they could also work in Latch. It’s accessible for both.”

The increased accessibility to data has led us to generate new therapeutic targets. It’s increased our ability to quickly query data to find out if we have expression of different markers, to identify new biomarkers for different types of pain, and to validate those targets.

Last July, we had 4-5 validated targets in our pipeline. In the year since then, we’ve increased that to 12-15 targets. The number of targets in our pipeline has tripled.

We have this entire menu of therapeutic targets we have validated now. And we are able to rapidly generate these in pain, and other areas of science.”