Editing Patch clamp (section)

=== [[Patch-sequencing|Patch-Seq]] ===
A combination of cellular imaging, RNA sequencing and patch clamp [[Patch-sequencing|this method]] is used to fully characterize neurons across multiple modalities.<ref>{{Cite journal|last1=Tripathy|first1=Shreejoy J.|last2=Toker|first2=Lilah|last3=Bomkamp|first3=Claire|last4=Mancarci|first4=B. Ogan|last5=Belmadani|first5=Manuel|last6=Pavlidis|first6=Paul|date=2018-10-08|title=Assessing Transcriptome Quality in Patch-Seq Datasets|journal=Frontiers in Molecular Neuroscience|volume=11|pages=363|doi=10.3389/fnmol.2018.00363|issn=1662-5099|pmc=6187980|pmid=30349457|doi-access=free}}</ref>  As neural tissues are one of the most [[Transcription (biology)|transcriptomically]] diverse populations of [[Cell (biology)|cells]], classifying neurons into [[Cell type|cell types]] in order to understand the circuits they form is a major challenge for neuroscientists. Combining [[Neuron#Classification|classical classification methods]] with [[Single-cell RNA-sequencing|single cell RNA-sequencing]] post-hoc has proved to be difficult and slow. By combining multiple data modalities such as [[electrophysiology]], [[Single cell sequencing|sequencing]] and [[microscopy]], Patch-seq allows for neurons to be characterized in multiple ways simultaneously. It currently suffers from low throughput relative to other sequencing methods mainly due to the manual labor involved in achieving a successful patch-clamp recording on a neuron. Investigations are currently underway to automate patch-clamp technology which will improve the throughput of patch-seq as well.<ref name="Bowlby" />