NEW YORK – A cell-targeting sequencing method developed at Rockefeller University is being used to find drug targets for neurological conditions such as Parkinson's and Alzheimer's diseases.
Nuclear enriched transcript sort sequencings (NETSseq) is a sample preparation method that uses antibodies or RNA probes to latch on to the nuclei of pre-determined cells of interest, capturing pools of them for downstream transcriptome profiling or even chromatin accessibility profiling with next-generation sequencing.
Nathaniel Heintz, the Rockefeller professor whose lab created the method, said it provides deep data on transcriptomes and even chromatin accessibility from post-mortem human brain tissue. The main benefit is the ability to find genes that aren't highly expressed overall, but whose expression is significantly different from non-diseased cells, making it complementary to other genomics methods including single-cell transcriptomics and even genome-wide association studies.
Cerevance, a pharmaceutical firm cofounded by Heintz in 2016, says NETSseq has helped it identify and characterize cell types in the brain, as well as druggable targets.
"We do that in a big way," Cerevance CEO and cofounder Brad Margus said. The firm, which has obtained an exclusive license to the technology, has profiled more than 7,000 healthy and diseased samples and found six targets for its own pipeline. For example, the firm has studied microglia in patients with multiple sclerosis and ALS and found genes expressed selectively in those cells. The firm has also partnered with Takeda to investigate gastrointestinal disorders related to the central nervous system.
Their approach has drawn the attention of investors including Takeda, Bill Gates, and GV, formerly known as Google Ventures. Earlier this month, the firm announced a $45 million Series B financing round led GV, Gates, and Foresite Capital.
Though the process of targeting cells is expensive and time consuming, Heintz admitted, "as reagents become known, people will use this a bit more than they are now," he said.
The seeds of NETSseq came from some of Heintz's previous work showing that each cell type in the human brain has a unique set of nuclear envelope proteins can be targeted with antibodies or RNA probes to help capture it. In a 2018 paper published in eLife, Heintz and then-postdoc Xiao Xu led a study showing that they could target cells from mouse cerebellums for transcriptome sequencing and also that they could apply it to human cells.
While methods like fluorescence-activated cell sorting have been able to target cells, even for single-cell analysis, some neurons with odd shapes have eluded capture. Now, with NETSseq, Heintz, Cerevance, and others can pool those cells and get deep data sets on transcription.
Xu, now employed at Cerevance, spent a year scaling up the laboratory approach to commercial viability. Their deployment of NETSseq uses post-mortem brain tissue samples, mostly from brain banks in the US and Europe, which are most likely to have the high-quality, clinically annotated samples required. The first step is to identify a gene or protein expressed on the surface of the cell nucleus or in the endoplasmic reticulum.
"There are quite a few you can look at" for each cell type, Margus said. "You only have to do it once for a cell type, but it takes hundreds or thousands of hours and you have to validate [the antibodies] a lot."
Once the cells are sorted on a flow cytometer, the firm uses one of several RNA-seq library preparation kits, but Margus declined to say which ones. In their 2018 publication, Xu and Heintz used the Nugen Ovation RNA-seq V2 kit to convert RNA to cDNA and the Illumina TruSeq DNA LT Library Preparation Kit or the NEBNext Ultra DNA Library Prep Kit for Illumina sequencing with NEBNext Multiplex Oligos. Cerevance then sends the libraries out for sequencing by a service provider.
Margus noted that Cerevance has applied for and obtained its own patents on its improvements to the NETSseq method beyond the original Rockefeller intellectual property.
"When you're doing it on a large scale, it's really different," he said. In addition to automating the bench steps, the firm has built a custom bioinformatics suite to analyze data and find disease signatures, combining standard tools with custom software to analyze terabytes of data. The firm placed special emphasis on data visualization.
"We don't want bioinformaticians to be the only people that can analyze data" Margus said. "We made it a principle that every scientist has to be able to access the data and interpret it. All the analysis software is built to make sure everyone can use it."
Cerevance continues to work to capture new cell types at its wet labs in Cambridge, UK, which it obtained, equipped, and staffed with help from Takeda. Margus and Heintz had previously cofounded another startup which developed a relationship with Takeda. In addition to coleading Cerevance's $21.5 million Series A financing round with Lightstone Ventures, Takeda provided the firm with licenses to a portfolio of preclinical and clinical drug programs.
NETSseq joins several technologies for profiling transcription in the human brain and central nervous system. Single-cell profiling methods have become wildly popular in many fields and neuroscience is no exception.
But single cells don't provide deep enough data sets to find the drug targets that Cerevance is after. "It's a really good technique for the top 1,000 most highly expressed genes in a cell type, but you just don't get enough RNA, so you can't see all the genes," Margus said.
"If only looking for markers, single-cell analysis is good," he added. "But if you're looking at targets, something that changes between healthy and disease, or expressed in only one cell type, you want to look at 10,000 to 12,000 genes."
But Margus and Heintz suggested the methods were complementary given their different strengths. Margus even suggested that NETSseq is useful to validate hits from genome-wide association studies when the assay for transposase-accessible chromatin using sequencing is used at the final readout.
"For a lot of common diseases, you get common SNPs supposedly associated with the disease, but you don't know how to prioritize studying them," he said. With NETSseq and ATAC-seq, "you can take the results from GWAS and add to it information about whether a SNP is actually being accessed in a specific cell type."
In his lab, Heintz continues to use NETSseq to study human brain cells. The method has also begun to spread, as Paola Arlotta and Constance Cepko at Harvard University have used a variation of it to study human cerebral cortex and cone photoreceptor cells in the retina (Arlotta did not respond to request for comment.) Heintz said he is open to sharing the reagents for capturing nuclei with academic researchers.
The method could even find application in developing companion diagnostics for Cerevance's drugs. Margus said the firm has one Alzheimer's disease drug target in a brain cell whose expression correlates with expression of another protein in the blood, suggesting a blood-based test could help select patients for that drug or stratify them in studies. Margus declined to say which protein it was.
But the method's main value proposition is to expand the universe of potential drug targets for diseases that affect thousands of people in the US, but don't have great treatment options.
"If you look at the industry for a disease like Alzheimer's, the whole world's working on just a handful of targets and the same old hypotheses," Margus said, namely the buildup of tau protein and amyloid plaques. "Everyone's working on that partly because they don't want to take risks on new targets and partly because they don't have a way to find new targets and along we come with a technology that can at last look deeply in human tissue, see changes, and reveal potential targets."