Simona Cristea Profile picture
Dec 9 13 tweets 6 min read
#singlecell analysis is revolutionizing medicine and changing the way we look at disease.

New perspective article just out🚨@NatureMedicine reflecting on @humancellatlas: informative for both #singlecell lovers❤️& skeptiks🤔

Let's map out where the field stands & what is next🧵
First, some context.

The genomics single cell field has started out 1-2 decades ago with a huge promise:

"Find the missing link between genes, diseases and therapies. This will bring completely novel therapeutics to the market & cure disease."
The underlying logic is straigtforward:

1. the cell is the main unit of living organisms
⬇️
2. cells break down in disease
⬇️
3. understanding cells helps understand how & why they break
⬇️
4. this helps with engineering new therapeutics
⬇️
5. new therapeutics will cure disease
But disease can only be understood in contrast to healthy tissues, which are already intrinsically complex.

For example, my recent work is proposing interventions on the healthy breast to prevent cancer initiation.

@humancellatlas aims to help by characterizing healthy tissues
Now, the progress.

1. Technological

There has been a truly impressive toolkit of experimental methods developed to construct cell atlases at different levels of biological organization, ranging from microscopy, spatial (RNA/protein), transcriptome, multimodal, genome/epigenome.
These methods are nicely organized & briefly described in the paper in a large table spanning 2 pages.
2. GWAS

Mutations in single genes have been found to be associated with many diseases in genome-wide association studies (GWAS). Further moving from single genes to groups of genes operating together (modules or programs) allows for more complex functional characterizations.
3. Cellular composition

Molecular characterizations (usually #scRNAseq) have mapped out the detailed cellular landscapes of multiple developmental processes & diseases.

Important to keep in mind that such landscapes were previously unknown, at least not in detailed large scale.
Cancer is an important example, where #scRNAseq has been heavily applied to characterize, by now, tens of thousands of tumors, sometimes together with normal tissue.

In particular, lots of deep inner workings of the immune system have been unraveled with single cell analyses.
This aspect is clinically relevant, as #immunotherapy is one of the most promising cancer therapies out there, however why and how exactly it works is not yet totally understood.

Works as the ones from @humancellatlas push therapeutics developments further, step by step.
4. Drug discovery

Pooled genetic screens with single-cell genomics readouts, such as Perturb-Seq, efficiently characterized the impact of single-cell profiles in perturbations across large number of genes in many tissues.

Huge potential in coupling this with large AI models.
How about challenges⛰️ ?

1. Cost of experimental methods still prohibitive for many labs
2. Experimental methods still not robust enough
3. Algorithmic #Bioinformatics development: more efficient & scalable
4. Diversity of human data gathered
Most important challenge: how to go past the descriptive nature of #scRNAseq atlases?

Lots of follow-up work needed to disentangle correlation from causation, both on the algorithmic, as well as on the experimental side.

Exciting times ahead!👏 @teichlab
nature.com/articles/s4159…

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Simona Cristea

Simona Cristea Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @simocristea

Dec 7
Can we outsmart #cancer and stop it before it even starts?

Our brand new paper🔥@NatureComms reveals a novel stem-like cell population directly related to #breast tumor initiation.

Let's dig in🧵🧵 Image
First, quick background.

Sadly, everybody reading this knows breast cancer.

It is the most commonly diagnosed cancer in women, with a staggering 1 in every 8 women in the world receiving this diagnosis throughout their lifetimes.
Multiple factors have been shown to modulate breast cancer risk.

You might already know that:

An active lifestyle🏃‍♀️, a good diet 🥦 or breastfeeding 🤱 are protective, while high breast density, radiation exposure or hormone replacement therapy are detrimental. Image
Read 27 tweets
Dec 5
Can't keep up with all the interesting #ChatGPT prompts?

Nothing to worry about! I curated a 🧵for you with key messages & relevant tweets on where our new academic companion #ChatGPT excels or fails in writing #Bioinformatics code, academic grants & tutorials👇
1. Our new friend is very good with writing #Python and #RStats code.

1.1 Here, it teaches us plotting with pandas & matplotlib, together with explanatory text.

If you are in the process of learning/improving your Python skills, #ChatGPT is of real help.
1.2 It is amazing that it can also debug code really well. It finds bugs, explains them, then fixes them!

Read 28 tweets
Nov 21
New🔥TCR #CRISPR✂️ cancer #immunotherapy paper just out in @Nature by PACT Pharma & UCLA.

Why all the 📸headlines & press coverage?

This small proof-of-concept study can give us a glimpse into the future of cancer therapy.

Let’s unpack the details & relevance of this study👇🧵
First things first:

Here’s the link to the paper, which has been made public by @Nature in an unedited form (before official publication), due to its perceived immediate relevance to the research & clinical cancer communities.

nature.com/articles/s4158…
A. The context ✍️ of this work:

Most cancerous lesions have mutations in the 🧬of their cells.

Cancer cells (literally) display small pieces of cellular mutated proteins (called peptides) on their surface.

These peptides, called neoantigens, are important therapeutic targets.
Read 21 tweets
Nov 7
New🔥 #DataScience #Bioinformatics resource: 850,000‼️ #scRNAseq cells from 226 samples across 10 cancer types draw a map of the tumor microenvironment, in particular fibroblasts.

Let’s see👇what are the main contributions of this work & what this means for #cancer #Genomics🧵 Image
But first, some background.

Cancers are (unfortunately) complex ecosystems,consisting of various types of cells.

Malignant cells represent only a fraction of the tumor. The rest is made of the tumor microenvironment/TME (fibroblasts + immune cells), with complicated dual roles. Image
Understanding the essence of this duality is key in understanding why most cancer therapies fail.

TME cells are plastic & can easily change states.

The same TME cells can either promote or suppress tumor development, depending on very subtle factors totally not well understood.
Read 22 tweets
Nov 1
#GraphNeuralNetworks are way too cool to be left unexplored!

In a nutshell, GNNs are an exciting merger between graph theory (math) & #DeepLearning (coding).

Here's my detailed resource stack of best GNN theory explainers, videos & coding tutorials I used for my own learning.
1. This is a great place to start if you either: want to learn the basics, or enjoy reading about basic concepts explained in a well structured way.

It walks us through graphs in real world, what graphs & GNNs consist of, and how GNNs do prediction.

distill.pub/2021/gnn-intro/
2. Further, this next tutorial walks us through graphs & GNNs in an intuitive manner, while also going quite deep into the specific mathematical terminology of the field.

I like this one a lot because it also includes hands-on PyTorch code at every step.
theaisummer.com/graph-convolut…
Read 15 tweets
Oct 31
It’s that time of the year🎃when students are deciding if to apply for #PhDpositions👻

Even though #Academia is far from perfect:doing a #PhD is valuable.

It develops unique & important skills that will stay with you forever

These PhD skills are totally☹️not discussed enough👇 Image
1. Ability to work interdependently🔁(independently & with others).

Doing a PhD requires individual planning, as well as collaborating and working within teams. The ability to dive really deep into both these areas *simultaneously* is one of the defining features of a PhD.
2. Self-motivation & drive

Doing a PhD can also be hard & frustrating😮‍💨. You'll sometimes find yourself working alone, with no clear goals on the project, and no end in sight.

Making it work somehow nurtures your ability to motivate yourself when faced with hard situations.
Read 12 tweets

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/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

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

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us on Twitter!

:(