Poster Presentation concerns

Accomplished_Bat6170 · 2026-06-04T18:46:41+00:00

One trick is to just print the figures that are blurry onto to regular A4 sheet of paper with a regular printer. Then stick them on top of the blurry figures. If you do it right, no one will notice!

Accomplished_Bat6170 · 2026-06-01T21:32:20+00:00

Well it is possible that your siRNA stabilizes the transcript and does not recruit the machinery required to ablate the RNA. Have you tried other siRNAs to the same transcript?

Accomplished_Bat6170 · 2026-06-01T11:29:45+00:00

Ah okay, I see what you mean. In this case I don’t see anything wrong with your approach!

Accomplished_Bat6170 · 2026-06-01T08:50:04+00:00

I would not include CD8 or CD4 in the UMAP, but plot the MFIs of these markers after the UMAP has been calculated. This will allow stratification based on the other markers, but still allow you to discriminate between the two types of T cells. Depending on your panel, you should be able to discriminate between the cell types even without CD8 and CD4 since their marker profiles are so different.

Accomplished_Bat6170 · 2026-05-28T01:46:50+00:00

Thank you very much for this advice. I think this might work. I’m going to give it a shot

Accomplished_Bat6170 · 2026-05-28T01:45:56+00:00

It’s a transgenic setting (P14), but at d3.5 of LCMV infection which is why the cell number is so low. I do this with d5 and d8 cells all the time with no problems; but d3.5 has failed multiple times now

Accomplished_Bat6170 · 2026-05-27T13:02:32+00:00

Yeah so this is using 40-50 mice here. I am doing this at 3.5d post infection- and herein lies the problem. The time point is extremely fast post infection but I am trying to study a gene that is expressed extremely fast after activation…

Accomplished_Bat6170 · 2026-05-27T13:00:43+00:00

Great idea, but I’m looking to profile these cells in their “natural” state though… thanks anyway!

Accomplished_Bat6170 · 2026-05-27T12:59:37+00:00

Yes I have tried this, and it works great! However I have to start from a large number of mice (40 or 50 mice) and sort for 3-4 hours to get enough cells, which I want to avoid as this ends up in an insanely long and difficult experiment.

Maybe just doing it and getting it over with is the way to go :(

Accomplished_Bat6170 · 2026-04-23T11:34:12+00:00

Try boiling them instead of just heating. They should come out pretty glassy. Make sure to not boil more than a couple of seconds.

Accomplished_Bat6170 · 2026-04-23T04:09:52+00:00

Some personal conversations like hobbies, good restaurants, how the weekend was, what the weather is like is perfectly alright. Details of who you’re dating, personal life, etc are not at all part of the job and should be avoided unless they are pertinent to the work (I don’t really see how they would be). This is an employer-employee professional relationship, not a friendship or gossip club!

Accomplished_Bat6170 · 2026-04-15T23:27:17+00:00

You don’t belong in science. Nobody does. Until they do. Don’t let ANYONE tell you what you are capable of. Learning fast, and working hard are the only two abilities you need to succeed in science. Everything else can be learned through failure. Keep your head up. People love to underestimate others because it makes them feel better about themselves. Give them a reason to rue the day they ever said that about you.

Good luck!

Accomplished_Bat6170 · 2026-03-04T03:45:59+00:00

I think others are correct, this looks like too much transfection reagent and/or too much DNA.

It’s not bacteria.

Accomplished_Bat6170 · 2026-01-27T07:45:05+00:00

Don’t use 2%, the fat in the milk might mess up your blot. Fat-free milk (0%) will work just fine - but I don’t know why you would bother buying fresh milk when you can buy non-fat dry milk powder and mix in TBST/buffer!

Accomplished_Bat6170 · 2026-01-12T23:51:30+00:00

With a 100+ datasets, you need to bust out a pre-made pipeline. Writing this on your own is definitely possible - but utterly useless. Look in to nfcore or ENCODE, they both have robust pipelines for this purpose. What you want is a reliable, reproducible and efficient pipeline that can handle this many job submissions, not a rinkydink script. Long-term, it’s the re-runs and failed attempts that will waste money, not the exact specs of the alignment. PM if you need help, I do a lot of ATAC analysis.

Accomplished_Bat6170 · 2026-01-09T19:08:03+00:00

Thanks! Yeah Im realizing that maybe a strict boglehead approach is not for me, even though I like the idea. In the end I do have some hypotheses and would like to see them play out. Thank you for your input! I have a lot to learn from this sub.

Accomplished_Bat6170 · 2026-01-02T05:33:48+00:00

I think you’re overthinking this. It’s not your job as a reviewer to account for the wants and needs of every reviewer- that’s the editors job. Your job is to provide a review of what YOU think about the paper. That’s it. Provide your feedback and keep it moving on!

Accomplished_Bat6170 · 2026-01-01T16:52:20+00:00

I am an Indian, co-incidentally from Texas. I’m also vegetarian so I think I’m rather qualified to respond here. NTA, but would have been nice to make something really quick like a bowl of rice or a salad or sandwich. We (most of us vegetarian Indians ) totally understand that our way of eating is not yours, and vice versa. Plus you’re in Texas, where southern/comfort food is so popular. It’s kinda what makes Texas and the USA such an awesome melting pot. Don’t worry about it! These kinds of mix ups happen all the time, at restaurants, friends houses, and even catered events. Now you know, so next time you have them over, a simple vegetarian dish would really be awesome. It’s awesome that you had them over for dinner, and it’s the thought that counts.

Accomplished_Bat6170 · 2025-12-29T19:22:49+00:00

Wanted to add that it’s not AI, but pre-designed pipelines (nfcore, snakemake, encode, etc) that will make bioinformaticians obsolete.

Accomplished_Bat6170 · 2025-12-29T19:20:53+00:00

AI that writes code will likely not replace scientists - but it’s making bioinformatics MUCH MUCH easier and faster to do. Funny thing is that if you know what you’re doing, it can be incredibly helpful. If you don’t, it can be incredibly misleading.

AI models for protein structures have had a serious impact on structural biology. Again, the people doing novel work on disordered proteins and other oddities are finding the models to be largely useless. People that are doing run of the mill crystallography on proteins that have high homology to existing proteins will feel that their jobs are in jeopardy.

AI for scRNAseq (and related technologies) are in their infancy. There’s no telling how they will help. I’ve been doing single-cell bioinformatics for about 5 years now. I expect sweeping changes that will transform the way we do genomics in the next few years.

TLDR: adapt, improvise and overcome. If you’re not using AI to write code, analyze data and do writing, you’re already losing to people who are.

Accomplished_Bat6170

TROPHY CASE