Discover and read the best of Twitter Threads about #scRNASeq

Most recents (24)

How wounds heal

#woundhealing #injury #homeostasis #angiogenesis #epidermal #skin #MedTwitter #meded #foamed #InnoMed

Timelapse Of A Wound Healing
#epidermal #medtwitter #meded #foamed #InnoMed #homeostasis #injuy #woundhealing #sciencetwitter

How does #aging affect #WoundHealing?
#scRNAseq identifies major changes in cell compositions, kinetics, and molecular profiles during #woundhealing in aged #skin @CellReports
#epidermal #MedTwitter #meded #foamed #InnoMed #bioinformatics #homeostasis
Read 4 tweets
Bench to bed series: Lung COPD part 3/3
#scRNAseq paper: Human distal airways contain a multipotent secretory cell that can regenerate alveoli
1. RASCs (new cell-type) + #stemcell properties in distal airways 2. faulty RASC-to-AT2 transformation in COPD
#Bioinformatics #meded Image
Read 4 tweets
For centuries we've had anatomical maps of how the body's organs are connected, but what would a diagram of the immune system’s connections look like? In @Nature we report our initiative to map the "interactome" that links human immune cells together (1/14)…
The life of an immune cell is constantly on the move. As white blood cells (leukocytes) circulate throughout the body, they must dynamically form connections with each other in order to communicate messages like “threat detected!” or “stop attacking, this is healthy tissue”(2/14)
Leukocytes can physically interact through #receptor proteins on their surfaces that have evolved to recognize and bind each other. These receptor interactions have enormous medical significance, to cite the #immuntherapy revolution in cancer treatment as just one example (3/14)
Read 16 tweets
Tons of exciting new single-cell genomics tools have been showcased at #bioc2022 this week. Today @LambdaMoses presented SpatialFeatureExperiment, an S4 class extending SpatialExperiment, facilitating geospatial stats for spatial #scRNAseq using Voyager… 1/ Image
The design of SpatialFeatureExperiment and the plans for Voyager were formed from a careful study that @LambdaMoses conducted of the spatial transcriptomics field (published as the "Museum of Spatial Transcriptomics"):… 2/
While there are several analysis tools for spatial transcriptomics data, and extensions of #scRNAseq platforms such as Seurat for spatial data, they have limitations in terms of the methods they implement from the field of geospatial statistics. 3/
Read 6 tweets
The exciting reveal of Ultima Genomics last week was accompanied by the publication of four preprints. Intrigued by the potential of the technology, @sinabooeshaghi & I decided to take a look at the data. A 🧵 about our findings & a preprint we posted:… 1/
We first looked at the company's own preprint on which the CEO is first author:…

Unfortunately, no data. No code. There is not even supplementary material, which the authors write "will be made available in the near future." 2/
Without data or code, obviously one cannot check the claims of the company. But in this case one cannot even understand the claims. E.g. the description for Fig. 2e in the Methods is useless without code to explain what was actually done to produce it. 3/
Read 25 tweets
Analysis of #scRNAseq requires constant, tedious, interaction with genomics databases. To facilitate querying from @ensembl et al., @NeuroLuebbert developed gget:… (code @
gget has many uses; a 🧵on the its amazing versatility: 1/
gget works from the command line or python. Just `pip install gget`.

Need reference files for your analysis? 2/
Simple with `gget ref`...3/
Read 25 tweets
The analysis of single-cell RNA-seq data begins with "normalizing" counts. In a preprint with @sinabooeshaghi, @IngileifBryndis & @agalvezmerchan, we examine the assumptions and challenges of normalization, benchmark methods, and motivate solutions:… 🧵 1/
We weren't particularly interested in studying normalization, but faced a vexing problem related to normalizing feature barcodes. In scouring the literature for solutions to our problem, we became increasingly confused rather than enlightened about how to normalize our data. 2/
We started with the excellent recent review / expository article by @const_ae & @wolfgangkhuber that looks at strengths & weaknesses of many methods:…. It became clear to us that a central question is how to normalize depth w/ gene count overdispersion. 3/
Read 25 tweets
Really chuffed to share our latest work from Sanjana Lab out in @nature today (…).

We tested >12,000 genes to find positive regulators of T cell proliferation to be used for next-gen #immunotherapies.

A thread...
(2/28) T cell therapies have shown the potential to cure patients from #cancer – but even in B cell cancers, relapses outnumber cures… One of the problems is limited T cell persistence. #celltherapy #CARTcell
(3/28) We wanted to find new genes (including genes never expressed in T cells) that could improve T cell persistence. Now, the question was: should we use #crispr activation or directly deliver target genes on a #lentivirus?
Read 30 tweets
How do metastatic #ovariancancer tumors change during #chemotherapy to survive, eventually killing the patient? See our longitudinal #scRNAseq analysis, now out @ScienceAdvances, with accessible data & tools.…
A 🧵 below (1/9)
We collected a unique set of paired, metastatic tumor specimens from 11 high-grade serous patients treated in @TyksVsshp (by @mijohy & colleagues) before and after neo-adjuvant chemotherapy, and analysed dissociated tumors with scRNA-seq. (2/9) Image
Unlike stroma or immune cells, cancer cells had distinctly patient-specific profiles. To find the hidden, shared states from these genetically heterogeneous tumor specimens, @KaiyangZhang @HautaniemiLab developed a new clustering method, PRIMUS (3/9)
Read 10 tweets
If you work w/ single-cell RNA-seq & are performing RNA velocity analyses, you might find this @GorinGennady et al. preprint w/ Meichen Fang & Tara Chari of interest. It's a deep dive into the method, and navigation of the 67 pages may be aided w/ this🧵1/…
As a starting point, it's worth noting that the two popular packages right now, scVelo (@VolkerBergen et al. from @fabian_theis' lab) and velocyto (@GioeleLaManno et al. from the @slinnarsson and @KharchenkoLab labs), yield discordant results on a simple example (see below). 2/
The inferred directions should recapitulate a known differentiation trajectory from radial glia to mature neurons. However, scVelo reverss the trajectory, despite "generalizing" velocyto & relying on a better model. Also sometimes it's scVelo that works well. So what gives? 3/
Read 27 tweets
Please RT:
Looking for a computational PostDoc to join our lab @UiTNorgesarktis in beautiful Northern Norway through the Artic Marie-Curie PostDoc program (…).

Apply by 25th of February.

#academicjobs #postdocs #microRNA #scRNAseq Image
Successful applicants will be invited to Tromsø (travel and accommodation expenses covered) for a three-day MSCA-PF symposium June 8-10, 2022 (if pandemic allows).
At this event, the candidates will present their past research achievements, discuss future plans with their potential supervisor, and learn how to write a successful MSCA-PF application.

(see:… the lab's current projects / latest publications) Image
Read 5 tweets
Is a single-cell RNA-seq atlas really an atlas? A short thread about #scRNAseq, maps, and atlantes (yes, the plural of atlas is atlantes! h/t @NeuroLuebbert). 🧵1/
Atlantes must be accurate to be useful, and the vexing question for centuries, namely how to best represent the spherical earth in 2D, is nontrivial. There have been many proposals with pros & cons for each (because the sphere and the plane have different Gaussian curvatures). 2/
In #scRNAseq, atlases of cells have become synonyms with UMAP figures of gene expression matrices (used to be t-SNE but UMAP seems more popular now). Map making from gene expression matrices is more challenging than map making of our 3D world; #scRNAseq is in ~10⁴ dimensions. 3/
Read 26 tweets
The 17 #BICCN @nature papers on the primary motor cortex in mouse (+some human & marmoset) that were published yesterday are a major step forward in terms of open science for an @NIH consortium. For reference, links to the open access papers are here:… 1/🧵
First, the #BICCN required preprints of all the papers to be posted on @biorxivpreprint, and as a result the papers were already online 1-1.5 years ago. Of course the final versions now published have been revised in response to peer review. 2/
Speaking of peer review, almost all the papers were published along with the reviews. In combination with the preprints, this provides an unprecedented view of how consortium work is reviewed and how authors respond. Real data for this perennial debate: 3/
Read 17 tweets
Early IFN-α signatures and persistent dysfunction are distinguishing features of NK cells in severe COVID-19,, our latest team effort in #COVID19 research lead by Jacob Natterman @LabSchultze and @AschenbrennerAC out now @ImmunityCP. [1/n]
We performed a detailed characterization of natural killer cells in 205 patients from four independent cohorts using #scRNAseq, #MCFC and #CyTOF together with functional studies. [2/n]
We found elevated IFN-α plasma levels in early severe COVD-19 alongside increased NK cell expression of ISGs and genes involved in IFN-α signaling. [3/n]
Read 8 tweets
It's time to stop making t-SNE & UMAP plots. In a new preprint w/ Tara Chari we show that while they display some correlation with the underlying high-dimension data, they don't preserve local or global structure & are misleading. They're also arbitrary.🧵… Image
On t-SNE & UMAP preserving structure: 1) we show massive distortion by examining what happens to equidistant cells and cell types. 2) neighbors aren't preserved. 3) Biologically meaningful metrics are distorted. E.g., see below: Image
These distortions are inevitable. Cells or cell types that are equidistant in high dimension must exhibit increasing distortion as they increase in number. Actually, UMAP and t-SNE distortions are even worse (much worse!) than the lower bounds from theory. ImageImage
Read 25 tweets
Super exciting and very laborious new study @berlinnovation (with @UKL_Leipzig and us @dkfz) on the question of why #children are so much less likely to develop severe #COVID19 than adults! Central point (experts may smile tiredly 😉): #interferon!
Our colleagues in Berlin studied cells in nasal swabs from a total of >80 people (single cell RNA sequencing, #scRNASeq)– 42 children and 44 adults, half of each were healthy and half #SARSCoV2-infected. What we found was intriguing: already in…
... healthy children, the cells of the nasal mucosa (epithelial and immune cells) were already on heightened alert! I.e. they had a significantly higher expression of the sensor system for #virus infection (esp. MDA5 and RIG-I). @VladimirMagalh in our lab was able to show ...
Read 9 tweets
Wirklich super spannende und sehr aufwändige neue Studie @berlinnovation (zusammen mit @UKL_Leipzig und uns @DKFZ) zur Frage, weshalb #Kinder so viel seltener schwer an #COVID19 erkranken als Erwachsene! Zentraler Punkt (der Fachmann wird müde lächeln 😉): #Interferon!
Die Berliner Kollegen haben die Zellen im Nasenabstrich von insgesamt >80 Menschen untersucht (Einzelzell-RNA-Sequenzierung, #scRNASeq)– 42 Kinder und 44 Erwachsene, je die Hälfte gesund und die andere #SARSCoV2-infiziert. Was wir fanden war faszinierend: bereits bei…
… gesunden Kindern waren die Zellen der Nasenschleimhaut (Epithel- und Immunzellen) bereits in erhöhter Alarmbereitschaft! D.h. sie hatten ein deutlich ausgeprägteres Sensorsystem für #Virus-Infektion (v.a. MDA5 und RIG-I). @VladimirMagalh in unserem Labor konnte zeigen, …
Read 10 tweets
1. Our work showing that GABA-receptive microglia selectively sculpt inhibitory synapses during development is out @CellCellPress:
Wonderful collaboration w/ @stevens1lab @Datta_Lab Farhi lab @RichBonneauNYU @harvardmed @broadinstitute thread⬇︎
2. Work from many labs has shown that #microglia are key regulators of synapse #development but we know that not all #synapses are created equal. The best example? Excitatory vs inhibitory synapses. Are microglia a jack of all trades or do they focus on specific synapse types?
3. At the outset, we were unsure whether microglia even regulate the development of inhibitory synapses. The answer to this was "they do”. When we depleted microglia during cortical development, we observed increases in both excitatory and inhibitory synapses
Read 10 tweets
Happy to announce that our (@zhao7900, @YinyingWang) #deeplearning method INSCT for integration of #scrnaseq is out @NatMachIntell! We extended triplet neural nets by defining 'batch-aware' triplet sampling, which overcomes batch effects in #scrnaseq data.
Instead of sampling triplets the traditional way, we added constraint to sample anchor-positives from different & anchor-negatives from the same batch. (We expect this approach to be also useful outside of #scrnaseq data - happy to discuss w anyone interested).
The semi-supervised mode allows robust transfer of cell labels across batches. Use case example: transfer labels from reference to new data set.
Read 4 tweets
Did you know #lipids control cell identity? Yes, they do! Happy to share our first preprint on #singlecell #lipidomics in collaboration with @gio_dangelo and a fabulous team led by @CapolupoLaura and Irina Khven.… Image
We used #MALDI imaging mass spectrometry to measure the single-cell lipidomes of hundreds of individual human dermal #fibroblasts. We identify specific lipid metabolic pathways that display cell-to-cell variability. Unexpectedly, single cells clustered by lipid composition! Image
It took a lot of experiments to convince us, but now we can say it confidently: there is such a thing as a #lipotype!! First of all, toxin-based lipid staining validated the existence of the different populations of dermal fibroblasts both in vitro and in vivo. Image
Read 10 tweets
This past week my lab published 4 @biorxivpreprint papers in applied math (…), biology (…), bioinformatics (…), and instrumentation (…). They were possible thanks to reproducibility... 1/
There is a lot of focus on the importance of reproducible science for facilitating replication of published research. That's all good, but reproducible science has another benefit: when adopted by a group it is an incredible accelerant for research *in that group*. 2/
Consider the paper we wrote on whole animal multiplexed #scRNAseq. The @GoogleColab notebooks Tara Chari wrote for the analyses were a monumental effort, but she did not start from scratch. 3/
Read 8 tweets
New preprint from our lab on whole animal multiplexed #scRNAseq (WHAM-seq) by Tara Chari, Brady Weissbourd & @JaseGehring et al. collaborating w/ David Anderson, Evelyn Houliston @Clytia_Vlfr, and @richcopley. A 🧵 about our proof of principle in🎐... 1/…
We show #scRNAseq can be used for "reverse genomics" to conduct low-cost *experiments*. Instead of sequence first ask questions later, we ask questions first & then sequence. We illustrate the approach w/ a starvation experiment using the emerging model Clytia hemisphaerica. 2/ ImageImage
We performed multiplexed #scRNAseq using the ClickTag approach developed in our lab by @JaseGehring (w/@sisichen, Matt Thomson, Jeff Park). The chemical multiplexing can be used on any tissue/animal and facilitates experiments with little batch effect. 3/…
Read 16 tweets
A thread about curated databases in genomics:

The first database I curated by hand was for my Ph.D. thesis. It consisted of a database of 117 orthologous human and mouse genes (this was in the late 90s before either genome was sequenced!). It's still up:…
Compiling this database was hard. It required combing through GENBANK, performing alignments to check for orthology, examine proteins for homology etc. The database was generated for benchmarking a gene prediction tool, but I found that the curation had much more value than that.
The process of compiling the database taught me a ton about the state of gene sequences in GENBANK, challenges in sequence alignment, functional annotation etc. I learned a lot making this database. Also others found it useful in derivative work:….
Read 12 tweets
There has been discussion over the past week about what the new @Apple M1 chip means for bioinformatics. Some have predicted the end of compbio on @Apple. Others are more optimistic.

We got a Mac Mini & @pmelsted easily compiled kallisto bustools #scRNAseq on it. Results below: Image
Several points:
1. Compilation of code on the M1 ARM architecture was easy for kallisto and bustools because they have few dependencies. In fact we did it before for the ARM Rock64 which is why this time there was no problem with the M1.
2. @Apple has done a great job with Rosetta 2. M1 emulating x86 is still faster than previous Macs. And the extra cores are great for running kallisto.…
Read 6 tweets

Related hashtags

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3.00/month or $30.00/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!