Identifying Antigenic Switching by Clonal Cell Barcoding and Nanopore Sequencing in <em>Trypanosoma brucei</em>
Bibliographic record
Abstract
spp. periodically changes its homogeneous surface coat of variant surface glycoproteins (VSGs) to evade host antibodies during infection. This pathogen expresses one out of ~2,500 VSG genes at a time from telomeric expression sites (ESs) and periodically changes their expression by transcriptional switching or recombination. Attempts to track VSG switching have previously relied on genetic modifications of ES sequences with drug-selectable markers or genes encoding fluorescent proteins. However, genetic modifications of the ESs can interfere with the binding of proteins that control VSG transcription and/or recombination, thus affecting VSG expression and switching. Other approaches include Illumina sequencing of the VSG repertoire, which shows VSGs expressed in the population rather than cell switching; the Illumina short reads often limit the distinction of the large set of VSG genes. Here, we describe a methodology to study antigenic switching without modifications of the ES sequences. Our protocol enables the detection of VSG switching at nucleotide resolution using multiplexed clonal cell barcoding to track cells and nanopore sequencing to identify cell-specific VSG expression. We also developed a computational pipeline that takes DNA sequences and outputs VSGs expressed by cell clones. This protocol can be adapted to study clonal cell expression of large gene families in prokaryotes or eukaryotes. Key features • This protocol enables the analysis of variant surface glycoproteins (VSG) switching in T. brucei without modifying the expression site sequences. • It uses a streamlined computational pipeline that takes fastq DNA sequences and outputs expressed VSG genes by each parasite clone. • The protocol leverages the long reads sequencing capacity of the Oxford nanopore sequencing technology, which enables accurate identification of the expressed VSGs. • The protocol requires approximately eight to nine days to complete.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".