(Most of) the team:
Team members who successfully completed a Patch-Seq analysis of respiratory control neurons are (left to right) Greg Smith, Margaret Saha, Prajkta Kallurkar, and Christopher Del Negro. Team members Tina Picardo and Yae Sugimura are not pictured.
by Joseph McClain
September 24, 2021
Patch-Seq is short for “patch-clamp, followed by next-generation sequencing”. It is a collaborative procedure that has only been carried out in a few laboratories. Christopher Del Negro says there is a good reason for this.
âYou have to have an extremely skilled team in absolutely every aspect, because if you have a weak link, a weak link! – everything fails, âhe said.
Del Negro is Professor in the Department of Applied Sciences at William & Mary. He is part of an interdisciplinary team that has successfully used Patch-Seq to distinguish the physiological activity of two sets of neurons that are important in the generation of respiration.
Patch-Seq’s successful work has led to a better understanding of neural control of breathing. The discovery has important and relevant implications for sleep apnea and other neuronal respiratory conditions.
The team was led by Prajkta Kallurkar, a graduate student from the Del Negro lab who obtained her doctorate. in 2021. The other members of the Department of Applied Sciences team are Tina Picardo, Principal Scientist and Greg Conradi Smith, Professor. Margaret Saha, Chancellor Professor of Biology, and Yae Sugimura, of the Faculty of Medicine at Jikei University in Tokyo, complete the team.
Kallurkar explained that the Del Negro lab studies the neural basis of respiration. Breathing is controlled by a surprisingly small area of ââthe brainstem known as the pre-BÃ¶tzinger complex. As small as it is, the pre-BÃ¶tzinger complex is full of neurons, and the lab wanted to identify the classes of neurons responsible for generating the rhythm and motor pattern of respiration.
She said their investigation requires a coordinated series of operations, rather than a single technique. Patch-Seq begins by recording the electrophysiological properties of neurons in the target area of ââthe pre-BÃ¶tzinger complex responsible for inspiration, inspiration. Sugimura came from Japan and trained Kallurkar on how to successfully extract a neuron and she helped the team set up the Patch-Seq registration and extraction process in less than a month.
âOnce we have recorded the electrophysiological properties, we then extract all the cellular content and then we do downstream RNA sequencing,â Kallurkar said.
Measurement of electrophysiological properties, a technique known as “whole cell patch recording”, and cell extraction were performed using robot-guided pipettes, necessary when working with targets. as small as a single neuron. The team sent their samples for sequencing, but first the cellular contents – the RNA, painstakingly pipetted from neurons – are transcribed backwards into complementary DNA by Picardo.
“We are actually targeting the RNA from the sample,” she said. âSo I use molecular biology techniques to generate complementary DNA and then make a library of it.
The process seems straightforward, if not straightforward, but Kallurkar and Saha point out the challenges Picardo faced in his part of the project, a procedure known as reverse transcription and library building. Many of the challenges arise from the unusually low amount of raw material.
âWhat Tina did was amplify less than a picogram of starting mRNA in a high quality cDNA library,â Kallurkar said.
Picardo recognized that a high sample failure rate is accompanied by such a painstaking procedure involving such a small amount of material: 18 successful amplifications out of about 150 samples.
âIt’s incredibly difficult,â Saha said. “Tina has to deal with so little material – and RNA degrades when you blink!” And she does all the quality control herself before proceeding with the sequencing. So it’s exceptionally difficult and an incredible achievement. “
The next in the Patch-Seq series of steps is sequencing. Samples processed by Picardo were sent to an external lab that “reads” the nucleotides in each cell’s cDNA library, then assembles those reads into text files, which are sent back to the W&M team.
Saha and Conradi Smith’s data-science duo helped guide the team through the study. They played a larger role in working with data from the sequencing lab, finding out which genes are represented in the sequences and which are differentially expressed.
Kallurkar said the data science team focused on the relevant genes in the two target classes of pre-BÃ¶tzinger neurons, labeled Type-1 and Type-2. Since their animal model is the mouse, data science work begins with a mouse reference genome.
Data analysts use mapping software to compare a particular nucleotide sequence from their sample cards to a known mouse gene. This is a âbig dataâ problem, addressed using genomic data science and the W&M supercomputer cluster. Kallurkar says the mouse’s reference genome contains around 55,000 genes, of which 33,000 are expressed in the group’s samples.
âFor perspective, we have over 33,000 genes in a single individual cell, and we have 17 cells,â Kallurkar explained. “So our matrix is ââ33,000 out of 17.”
She said that within this matrix, the researchers are looking for genes belonging to two sets of neurons with different electrical properties. The samples contained one set of seven neurons and a second set of nine neurons.
âSo, now we want to know: of those 33,000, how many are differentially expressed between these two sets of neurons? Kallurkar said. She explained that if a particular gene is strongly expressed in one type of cell, and it is not expressed in the second, it shows that the gene is important for that particular type of cell, “and we should look for that. particular gene in order to target this population ‘
âThe data science side,â Conradi Smith commented, âis a heavy burden. Neural transcriptome studies are big data par excellence. Prajkta’s success required proficiency in scripting in Python, familiarity with several sophisticated bioinformatics software packages, and a deep understanding of statistical theory.
The Patch-Seq technique was developed about five years ago and Del Negro said only 25 studies have used this technique.
“And of those 25 studies, only two other studies did so from a population that has known behavioral function,” he added. âAnd those were pancreatic islet cells and retinal cells. Everyone knows that this population of neurons generates the respiratory rate and expresses the initial stages of the respiratory motor pattern. I think this is another important aspect of this work.
Employees have a paper in preprint on the Cold Spring Harbor Lab bioRxiv server. Del Negro says the work is so new and so interdisciplinary that finding the right refereed journal for it is a challenge.
âPatch-Seq is still a somewhat new technique,â ââhe said. âAnd we go around, trying to find the right place. I think we just haven’t found the right audience yet.
Del Negro adds that the focus of the study – type 1 and type 2 neurons – are subsets of a larger class known as Dbx1 – was discovered in 2010 by Picardo and was the subject of his doctoral thesis.
âThis is a follow-up to a decade of efforts to understand this singular population with known behavioral function,â Del Negro said. “And it’s a common thread that spans decades.”