Wildtype One

Proteomics and mass spec are going to take over current limited, low-throughput techniques

This field is not slowing down soon

Thanks for sharing this state of the art work @silviasurinova.bsky.social, looking forward for more

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💡 Bottom line:

+10 extra minutes for cell counting and documentation = Fewer failed repeats + cleaner data

❌ Cell seeding is not a “trivial detail”

✅ It’s a biological variable

Controlling it is one of the cheapest ways to improve reproducibility

(9/n)

• Document absolutely everything: passage number, confluence at treatment, and incubation time

• Train interns and ensure everyone counts cells accurately

• Automate counting or seeding systems for consistency

(8/n)

🔍🧰 So...

• Add a extra well (or two) with varying density (e.g., 30 % vs 90 % confluence)—if readouts change, density is a real variable

• Define a target seeding density (cells /cm² or % confluence)

• Time it precisely; e.g. “Treat 24 ± 1 h after plating”

(7/n)

If results differ by who did the experiment, this may be why...

One person plates denser

So...

(6/n)

🧨 Where It Bites

• Western blots and qPCR baseline expression shifts with confluence

• Drug and apoptosis assays density changes sensitivity and baseline death rates

• Transfection efficiency and signaling vary with cell cycle and confluence

(5/n)

Example:

Lab A plates 10k cells/well

Lab B plates 50k cells/well

Both treat at 24 hours

But Lab B’s cells are nearly confluent

Lab A’s are sparse

The results won’t match

(4/n)

Here's what happens: 💡

Cells sense neighbors through contact and paracrine signals

↗️ High density triggers quiescence, differentiation, or contact inhibition

↘️ Low density drives stress, altered signaling, and hyper-proliferation.

(3/n)

Young researchers rarely record these details

They're rarely mentioned in "Materials and Methods"

⚠️ Yet tiny confluence differences can derail experiments

So before fixing your Western blot + flow cytometry panel + survival curves + immunostaining + ...

Let's start with your plates

(2/n)

This is almost unbelievable

But cell density is a silent trap

(a thread)

Ruxolitinib seems reassuring for malaria research

Let's see how larger-scale evaluations unfold

Thanks for sharing the positive news @massimogg.bsky.social

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💡 Instead, use stacked bars

Why?

The human brain reads lengths much more easily than angles

Pie charts crowd your slides

By turning them into a stacked bar, you can easily fit multiple pie charts into one bar chart

You save space and make your work much clearer

(2/2)

A quick tip from Wildtype One: Stop using pie charts

Especially when you're showing evolution, like:

- "Before vs. After"
- "2020 vs. 2025"
- Flow cytometry cell population changes

❌ Pie charts are hard to read

(1/2)

Targeting miRNA signaling in vascular disease makes more sense after reading this

The publication elegantly linked miR-26b deficiency to aortic calcification

mir-26b really said ‘if i go down, the aorta goes down with me’

Nice work and thanks for sharing @onehealthgenomics.bsky.social

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Researchers: It’s the end of the year—this means expiring licences

Do you have a plan to avoid work interruption? Save cost? Choose the best software?

We found a solution

Read today's Wildtype Weekly in 3 minutes or less 👇

Kind of a striking demonstration of how subtle shifts in apical–basal tension can destabilize the neural plate and trigger live cell extrusion

Cell extrusion without apoptosis is honestly peak plot twist

Nice work. Thanks for sharing, @fzolessi.bsky.social

What gift should you buy your labmates for Christmas? 🎅🔬

Here are 9 budget-friendly Secret Santa ideas for researchers 🎁👇

It's nice to know where current deep learning tools succeed and where they distort biology

Nice work and thanks for sharing @vjsanchez.bsky.social

Western blot? In THIS economy?

At least cave people didn’t have to write rebuttals...

But keep in mind

Not all filaments are moldy

It can be ECM action, salt, or protein precipitation

Don't follow generic advice

Think a step further

— Wildtype One 🧬

💡

If you see filaments, investigate

If one flask is contaminated, don't just throw it away and keep using the rest

Check the:

- incubator water pan
- humidifier
- CO₂ supply line
- bottle caps
- serum bottle necks
- reagent lots
- old or poor-quality serum
- fibronectin coatings

(6/n)

❌ They do not correlate the appearance of filaments with any subtle change in cell behavior (growth, morphology, viability)

Yet the contamination may be subtly affecting data even if cells “look normal”

(5/n)

❌ They discard filaments as “just debris” without checking whether they are alive/growing

They ignore the source of the filaments (water, humidifier, air, serum, pipettes)

...and just keep using the culture

(4/n)

❌ They also assume “if I don’t see white fuzzy colonies, it’s not mold”

But some molds start with thin hyphae that look like filaments under microscopy

...but not obvious to naked eye

(3/n)

❌ Researchers often mis-interpret filaments

They assume “if medium is clear and there's no color change, it’s fine”

Filamentous fungal contamination may not cause immediate turbidity

(2/n)

You’ve probably heard nutritionists say:

"If the first slice of bread is moldy, you shouldn’t just throw it away and eat the rest"

"The whole bag may already be contaminated"

Same thing with cell culture

(but not always!)

...

(1/n)

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⚠️

Keep the opposite in mind

Just because the medium is clear

Doesn't mean there is no contamination

Remember the bread example:

Only the first piece of bread can be moldy

But the whole bag can be contaminated

Same thing with cell culture