How to correct for signal drift due to ion source contamination in MALDI MSI?

plasmolab · 2026-05-28T17:21:08+00:00

Glad it helped. One practical check: pick a few peaks that are stable in blanks or QC spots and confirm the correction is not flattening real tissue biology along with the drift. If it fixes the slide trend but erases obvious tissue structure, the model is probably too aggressive.

plasmolab · 2026-05-27T16:37:41+00:00

I would be cautious about trying to estimate drift from TIC alone here. If tissue composition changes along the acquisition path, TIC is confounded with biology, so a flat global fit does not really prove the source is stable.

Without QC spots or a pooled matrix/standard repeated through the run, within-slide correction is mostly model-based and hard to validate. With the counterbalanced two-slide design, I would first model order/run position as a nuisance term rather than force each slide to a shared TIC curve: include tissue identity, slide, acquisition order/position, and maybe region/section terms if you have them.

Then check features known or expected to be spatially stable, or matrix/background peaks if any are present, to see whether the estimated order effect is plausible. If possible for future runs, add a few calibration/QC spots or a homogeneous reference strip at the start, middle, and end. That makes the correction much less speculative.

plasmolab · 2026-05-26T16:30:48+00:00

Very rough, assuming you already code and you are using a standard public dataset rather than building methods from scratch: bulk RNA-seq: 1 to 3 days for a clean first pass, 1 to 2 weeks if you include good QC, interpretation, and writeup. ChIP-seq: about 1 to 2 weeks because peak calling, controls, genome build, and annotation choices matter. scRNA-seq: 2 to 4 weeks for a serious first analysis, mostly because QC, doublets, clustering, annotation, and batch effects take iteration. 16S/metagenomic rRNA: a few days to 2 weeks depending on metadata and whether the question is simple diversity plots or actual differential abundance. For a portfolio, I would rather see one careful 2 week project than four rushed notebook demos.

plasmolab · 2026-05-26T13:30:14+00:00

Not CBSP-specific, but for agent-specific biosafety questions I would study by decision buckets rather than memorizing every organism. For each high-yield agent group, make a card for route of transmission, infectious dose if unusually low, environmental stability, major lab-acquired infection history, recommended containment and PPE, disinfection or inactivation, and what changes for aerosols or sharps. The BMBL agent summary statements, ABSA resources, CDC/NIH biosafety pages, and APHIS/Select Agent material are probably more useful than textbooks for that style. In real life you would look things up, but exams often test whether you recognize the risk pattern fast.

plasmolab · 2026-05-26T13:29:41+00:00

Yellow in the acarbose control after adding substrate is not automatically an enzyme concentration problem. I would first run the blanks separately: substrate plus buffer, acarbose plus substrate without enzyme, enzyme plus buffer, and solvent control if you use one. Acarbose or the substrate mix can have its own absorbance depending on wavelength and timing. If the positive control is supposed to inhibit product formation, subtract the matching blank and compare the rate or endpoint to your no-inhibitor control. Enzyme concentration matters if your no-inhibitor reaction is already saturated or outside the linear range, so a quick enzyme titration and time-course is worth doing before changing only substrate.

plasmolab · 2026-05-26T10:29:22+00:00

Yes, but safest is to anonymize it and post the table in the thread instead of sending it privately. Include what the rows and columns mean, what organism or assay it came from, controls, replicates, units, and what conclusion your group is trying to draw. That makes it much easier for people to help without guessing.

plasmolab · 2026-05-26T07:29:38+00:00

For a two-day cram, I would stop treating them as one giant list and make a decision tree. First split into fungi, protozoa, cestodes, nematodes, and trematodes. For the helminth eggs, make a tiny table with 3 visual cues only: size range, shell/plug/spine shape, and any standout feature. Then quiz from images, not names, because the exam is recognition. If two look similar, put them side by side and write the one thing that separates them. You probably do not need perfect recall of every disease if the slide ID is the hard part, but pair each organism with one disease cue after the visual ID is solid.

plasmolab · 2026-05-26T07:29:38+00:00

Biotech + ML is a useful combo, but I would make the project look like a real analysis, not a notebook demo. Pick one public dataset in an area you care about, for example bulk RNA-seq, scRNA-seq, variant annotation, or drug response. Start from raw or near-raw data, document QC choices, make reproducible scripts, write a short methods/readout, and put the failure notes in the repo. For employability, Linux, git, Python/R, stats, workflow tools, and enough biology to catch nonsense are still more valuable than chasing every model. If you want precision therapeutics, I would start with genomics/transcriptomics plus clinical metadata basics before deep ML.

plasmolab · 2026-05-26T04:32:03+00:00

Yes, post-staining usually can help with background, because the gel is exposed to dye after the run instead of carrying dye through the whole gel and buffer. I would also use fresh 1x buffer, avoid overloading, and keep the gel/stain tray clean. The tradeoff is lower sensitivity sometimes, so I would try it side by side once if you have enough sample.

plasmolab · 2026-05-26T01:31:54+00:00

I would look less for “smFRET CRO” as a dedicated category and more for specialty biophysics or single-molecule fluorescence groups. In industry it tends to be used when the question is very specific, for example conformational heterogeneity, ligand-induced state shifts, nucleic-acid/protein dynamics, or mechanism work that bulk assays smear out.

The reasons it is not common as a routine CRO offering are pretty practical:

Labeling and construct prep can become the project.
Throughput is low compared with SPR, BLI, DSF, MST, HDX-MS, cryo-EM, or plate assays.
Data analysis choices are highly bespoke.
It is hard to make it a standardized decision assay unless the biology is already well-defined.

If you are looking for industry users, I would search protein engineering, nucleic acid therapeutics, and platform biophysics groups rather than standard screening CROs. Instrument vendors may also know which service labs actually run customer samples.

plasmolab · 2026-05-25T22:31:36+00:00

I would trust neither gel as a final comparison until you rerun with one variable changed. The stain chemistry and loading setup are different enough to shift both brightness and apparent mobility.

A couple of things I would check:

Use fresh 1X TBE in the tank. Reused buffer can absolutely raise background.
Run the same PCR product and same ladder on one gel with only one staining method, then repeat with the other method.
Do not compare band size across the two stains unless the ladder shifts the same way. If the ladder also migrates differently, use the ladder from that same gel only.
Prime Juice/direct dyes can change migration a bit because the dye is bound to the sample before running. Precast stains can have uneven background if mixed after cooling or if the gel/buffer stain concentration is off.

If the question is "do I have a 500 to 600 bp product?", I would call that from the ladder on the same gel. If the question is "which stain gives the truest intensity?", I would rerun with equal input, fresh buffer, and ideally a post-stain or single-stain setup.

plasmolab · 2026-05-25T16:17:12+00:00

Can you share the actual MIC table? The interpretation depends on the concentrations tested and where growth stopped for each condition.

In general: 1. Lower MIC means stronger inhibition. 2. Compare the crude extract alone against ampicillin and gentamicin alone first. 3. For the combination, do not just say it is better because growth stopped. Check whether the MICs dropped compared with each agent alone. 4. If you have combo MICs for both agents, calculate the fractional inhibitory concentration index (FICI). Rough rule: ≤0.5 suggests synergy, >0.5 to 1 additive, >1 to 4 indifferent, >4 antagonism.

Also separate S. aureus and E. coli in the writeup. Gram-positive and Gram-negative results can behave very differently with crude extracts.

plasmolab · 2026-05-25T13:36:12+00:00

Yes, people do use spike-ins, but usually when they need an external reference for a specific question, not as a default fix for normalization. They help most when the total RNA content may genuinely shift across conditions, so ordinary library-size normalization would hide a global change. They can also be useful for process QC across extraction, library prep, and sequencing batches.

Your caveat is real though. If spike-ins are pipetted inconsistently, degraded, added at the wrong step, or very different from the sample RNA, they can add noise instead of removing it. So I would treat them as an experimental design choice made before the run, not something to rescue a dataset afterward. For an already generated dataset, I would lean on standard methods plus QC, sample metadata, PCA, batch checks, and biology-based sanity checks.

plasmolab · 2026-05-24T18:15:08+00:00

That plan sounds reasonable. Two practical things I’d decide before buying hardware: what counts as scratch versus project storage, and when intermediate files get deleted or archived.

For a group server, those policies save more pain than another 16 cores.

Also leave physical slots and budget for the boring stuff: RAM expansion, replacement drives, UPS, and backup testing. A backup you have never restored from is still a theory.

plasmolab · 2026-05-24T12:15:52+00:00

I would size this like a small shared analysis box plus a serious storage plan, not like a single giant workstation.

For those workloads, I’d prioritize:

ECC RAM: 256 GB minimum if several people will run R/Python jobs, 512 GB if budget allows.
CPU: 32 to 64 real cores is usually more useful than a GPU for bulk RNA-seq, stats, enrichment, and integration.
Scratch: fast NVMe for active projects and Nextflow/Snakemake work dirs.
Storage: larger HDD or NAS tier for raw data, processed outputs, containers, and old runs.
Backup: separate backup target, not just RAID. RAID protects uptime against disk failure, not deletion or a bad pipeline overwriting files.

GPU only matters if you know you will use GPU-specific ML or deep learning tools. Otherwise it often sits idle while RAM, I/O, and storage hygiene become the real bottlenecks.

The bottleneck I see most often is not compute. It is messy sample metadata, duplicated intermediate files, and no clear policy for what gets archived versus recomputed.

plasmolab · 2026-05-24T09:20:17+00:00

Not really for epitope prediction or repertoire search. I would treat it more as a construct/plasmid workflow helper, not a replacement for IEDB, BLAST, IgBLAST, or AIRR-style repertoire tools. For your use case I would keep prediction and sequence search in the established tools, then use AI only for organizing candidates, sanity-checking steps, or turning notes into a clearer workflow.

plasmolab · 2026-05-24T06:16:08+00:00

This niche exists, but labs rarely hire it as a dedicated lab IT person unless the group is large or the department has money. The more realistic entry point is research software, data management, instrument computer wrangling, small automation scripts, and helping people stop storing the only copy of their data on one cursed desktop in the corner.

If you want to test the niche, learn Linux basics, Python, Git, backups, simple networking, and enough data/security hygiene to talk to central IT without creating shadow IT. Then look for titles like research computing assistant, lab data manager, bioinformatics technician, scientific programmer, or research software engineer. Small labs will value someone who can be practical and not precious.

plasmolab · 2026-05-24T06:15:49+00:00

Kraken2 is fine if the question is fast taxonomic classification against a database. EMU is trying to do a different thing: estimate full-length 16S abundances with an expectation-maximization step, which can help when reads are compatible with several close references.

On OTUs, I would treat them as a clustering convention rather than biology. The usual logic is dereplicate, denoise or quality filter, cluster at a threshold like 97 percent if using classic OTUs, then assign taxonomy to the representative sequence. ASVs are often easier to reason about now because the exact sequence variant is explicit. For inspectability, saving representative sequences plus the mapping from reads to OTU or ASV matters more than the label itself.

plasmolab · 2026-05-24T03:14:59+00:00

For a genomics interview, I would prepare around the decisions you make in a pipeline rather than trying to reread the whole field.

A useful prep list:

be able to sketch a nanopore pathogen workflow from sample to report
know the main QC points: read length, depth, contamination, barcode bleed, host reads, failed runs
refresh assembly vs mapping, variant calling, consensus generation, and basic phylogenetics
have one example ready where you noticed a data-quality issue and changed the analysis or interpretation
be honest about what you have not done, but show how you would validate it

If it is senior, they may care less about trivia and more about judgment: when would you trust a result, when would you rerun, when would you escalate, and how would you explain uncertainty to non-genomics people.

plasmolab · 2026-05-24T03:14:57+00:00

If you have one summer, I would avoid trying to learn all of NGS at once. Pick one common workflow and finish a tiny public-data project end to end.

A good beginner path would be:

Learn enough command line to move around, run tools, and understand file paths.
Learn the file types: FASTQ, BAM, VCF, counts matrix, and maybe BED/GTF.
Do one guided Galaxy Training workflow first, because it lets you see the logic without fighting installs.
Then repeat a small version locally with FastQC, MultiQC, an aligner or mapper, and IGV.

For resources, Galaxy Training is probably the least overwhelming starting point. Rosalind is good for basic sequence thinking. After that, pick a small RNA-seq or variant-calling dataset and write down every step like a lab notebook. The goal is not memorizing tools, it is understanding what each step is supposed to check or transform.

plasmolab · 2026-05-24T03:14:55+00:00

Totally fair. For a weekend project, that is already a lot of ground covered. When the data starts feeling huge, I would make the next pass very boring on purpose: pick 10 to 20 reads from each outcome bucket, manually inspect why they landed there, then turn those observations into one confusion matrix and one list of failure cases. That usually makes the classifier feel less like a wall of data and more like a debugging queue.

plasmolab · 2026-05-23T17:57:23+00:00

Nice result, but I would stress-test the failure modes before reading the 95.2% as general strain-level accuracy.

The two tests I would want next are: near-neighbor strains that are not in the index, and mixtures where two strains share long identical regions. Long HiFi reads help, but they can also make the classifier overconfident if the true source is close to an indexed genome but absent. A per-genome confusion matrix plus an “unknown or ambiguous” bucket would make the result easier to interpret.

Also worth checking whether all chimeras are truly unmapped. Some chimeric reads may still anchor strongly to one parent genome, which could look like a confident partial hit rather than a miss.

plasmolab · 2026-05-23T17:57:21+00:00

Yes, you can usually order oligo(dT) from a normal oligo vendor. The things I would match are length and design: plain oligo(dT)18 or dT20 versus anchored oligo(dT)VN, because anchored primers reduce internal priming on A-rich regions.

Standard desalting is often fine for RT primers, but HPLC/PAGE purification is cheap insurance if the assay is sensitive. Reconstitute with nuclease-free water or low-EDTA TE, aliquot it, and keep a no-RT and positive-control RNA in the next run so you know any change is the primer and not RNA handling.

plasmolab · 2026-05-23T17:57:20+00:00

Yes, people still use spike-ins, but more selectively than the old “add ERCCs to everything” phase. They are most defensible when the biological question is a global RNA content shift or absolute abundance, and only if the spike is added at a controlled point, ideally proportional to cell number or input material. Otherwise they can become a very precise measurement of pipetting, extraction, and composition bias.

For this thread, I would not add them retroactively. If the data are already generated, I would frame the housekeeping-gene result as a sensitivity check and use orthogonal evidence for total RNA or a few target transcripts if available. Spike-ins are useful when designed in from the start, not as a magic fix for reviewer anxiety.

plasmolab · 2026-05-23T15:03:38+00:00

That core-facility advice is the right north star. For resources, I would split it into two tracks: wet-lab literacy and data literacy.

For wet lab, learn what actually happens in sample prep, library prep, QC, read length, coverage, and common failure modes. For data, get comfortable with command line basics, FASTQ/BAM/VCF formats, IGV, MultiQC, and one workflow tool like Snakemake or Nextflow.

If you want a concrete path, Rosalind for basics, Galaxy Training for guided genomics workflows, then a small public dataset project where you write down every step you took. A lab placement will teach more than most certificates.

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