Polysome profiling analysis normalization and statistics best practices

Introduction

Scope: This guide focuses on rigorous, auditable normalization and statistical analysis for polysome profiling.

Audience: RNA-seq–savvy translational regulation teams in academia and pharma who need reproducible fraction-level workflows.

Outcomes: Standardized UV254 preprocessing, robust molecular normalization (with recovery-aware spike-ins), and negative binomial GLM-based redistribution tests that support reliable differential polysome association across gradients.

What does it take to turn a visually clean gradient into defensible, cross-sample comparisons that reviewers can audit end to end? This article answers that with concrete steps, code snippets, and QC thresholds you can adopt tomorrow.

Overview of sucrose-gradient polysome profiling from cycloheximide-stabilized lysis to ultracentrifugation, UV254 monitoring, fractionation, and fraction-level RNA extraction for RT-qPCR/RNA-seq. Fraction boundaries and per-fraction AUC regions are indicated to preview normalization and statistical analysis steps.

Key takeaways

• Apply baseline subtraction and peak-preserving smoothing to UV254 traces before computing fraction AUCs and percent ribosome density.
• Use pre-lysis internal spike-ins and estimate per-fraction recovery; scale counts by recovery and carry offsets into GLMs.
• Model redistribution with negative binomial GLMs across ordered fractions (designs with condition×fraction or condition×bin interactions and spike-in offsets).
• Balance replicates and use ComBat/RUV cautiously after spike-in scaling; verify improvements via PCA and replicate concordance.
• Track QC metrics: rRNA proportion, UV peak resolution, fraction purity/RIN, and replicate agreement thresholds.
• Report baseline-subtracted traces, fraction barplots, redistribution plots, and a methods transparency checklist.

UV254 trace processing

Baseline correction

Raw UV254 traces often exhibit slowly varying background due to scattering and drift. Two practical correction approaches are widely used:

• Reference-wavelength subtraction: Subtract scaled absorbance measured at a non-aromatic wavelength (e.g., 550 nm) from A254 to compensate for turbidity. A simple form is A254_corrected = A254_raw − k × A550, where k is empirically tuned (0.5–1.5) to minimize negative baselines and match blank gradients; see general UV–Vis compensation practices summarized in peer-reviewed literature.
• Baseline fitting: Fit a smooth background to a blank gradient or use rolling-median/asymmetric least-squares to estimate drift, then subtract prior to smoothing; validate by overlaying raw vs. corrected traces to ensure peak morphology is preserved.

Peak smoothing and AUC per fraction

Use peak-preserving smoothing (e.g., Savitzky–Golay with polynomial order 2–4 and window 11–21 points) to reduce high-frequency noise without distorting subunit/monosome/polysome peaks. Integrate the baseline-corrected, smoothed trace within recorded fraction boundaries to derive AUC per fraction. Percent total ribosome density for fraction j is:

%Density_j = 100 × AUC_j / Σ_k AUC_k.

This produces comparable semi-quantitative density profiles across gradients, supporting downstream binning and occupancy metrics.

Percent total ribosome density

For cross-sample comparisons, align monosome (80S) peaks—either by cross-correlation to a reference trace or by parametric peak fitting (Gaussian/Voigt) to locate apex positions and reindex fraction labels. Alignment stabilizes percent-density estimates when higher polysome peaks partially overlap.

UV254 processing workflow. Panel A: raw trace with baseline drift. Panel B: corrected trace after baseline subtraction. Panel C: monosome (80S) peak alignment across samples. Panel D: fraction boundaries annotated with shaded AUC regions and percent total ribosome density.

Molecular normalization with spike-ins (polysome profiling normalization)

Fraction-level spike-in strategy

Introduce a cross-species total RNA or synthetic mRNA spike-in into lysates before fractionation to track recovery across fractions. Define per-fraction expected spike amount S_exp (typically constant if equal volumes are collected) and observed spike abundance S_obs (from RT-qPCR or RNA-seq). The recovery factor is R_j = S_obs,j / S_exp. Scale biological counts per fraction by recovery: C′{g,j} = C{g,j} / R_j. For count-based models, pass log(R_j) as an offset to preserve NB mean–variance relationships while accounting for fraction-specific recovery.

Recovery and rRNA depletion checks

• Recovery diagnostics: Plot R_j across fractions; flag outliers with R_j < 0.2 × median(R) for re-extraction or exclusion.
• rRNA depletion: Evaluate per-fraction rRNA by electrophoresis (Bioanalyzer/TapeStation) and read alignment to rRNA references (e.g., sortMeRNA). As a practical target for polysome profiling normalization, aim for <5–10% rRNA post-depletion; fractions exceeding 20–50% may require deeper sequencing or reprocessing. For a concise overview of poly(A) enrichment vs rRNA depletion trade-offs, see the CD Genomics explainer on choosing depletion strategies: poly(A) enrichment vs rRNA depletion.

Integrating RT-qPCR/RNA-seq per fraction

Normalize RT-qPCR targets by spike-in markers at the fraction level; optionally compute a fraction-weighted position (FW) metric: FW = Σ (fraction index × % signal). ΔFW between conditions is an intuitive summary of redistribution. Cross-validate with RNA-seq by deriving per-fraction size factors or offsets from spike-in recovery, then confirm directional consistency for key transcripts.

Statistical models for redistribution

NB GLM across ordered fractions

Model per-gene counts across ordered fractions with negative binomial GLMs that include condition×fraction interactions and per-fraction offsets from spike-in recovery. For gene g, sample s, fraction f:

log μ_{g,s,f} = log(sizeFactor_s) + offset_{s,f} + β_{g,condition(s)} + γ_{g,fraction(f)} + δ_{g,condition×fraction(s,f)}.

• offset_{s,f}: log(R_j) from recovery (and any volume scaling), per sample×fraction.
• fraction: categorical (mono/light/heavy) or ordinal indices; interactions capture condition-specific shifts.
• Testing: use quasi-likelihood F-tests in edgeR or Wald/LRT in DESeq2 on interaction contrasts to detect redistribution. For GLM documentation, consult the authoritative manuals: edgeR user guide and DESeq2 vignette.

Example (edgeR, R):

# counts: genes × (samples × fractions)
library(edgeR)
# y: DGEList with counts; design encodes condition and fraction plus interaction
y <- DGEList(counts)
y <- calcNormFactors(y)
# offsets: matrix matching y$samples, built from log(R_j) per sample×fraction
y$offset <- y$offset + offset_matrix

design <- model.matrix(~ condition + fraction + condition:fraction)
y <- estimateDisp(y, design)
fit <- glmQLFit(y, design)
# Contrast: interaction terms for heavy vs mono under condition B vs A
contrast <- makeContrasts((conditionB.fractionHeavy) - (conditionA.fractionHeavy), levels=colnames(design))
res <- glmQLFTest(fit, contrast=contrast)
summary(decideTests(res))

DESeq2 alternative:

library(DESeq2)
# dds: DESeqDataSet with counts and colData including condition and fraction
design(dds) <- ~ condition + fraction + condition:fraction
# supply offsets (per sample×fraction) derived from log(R_j)
offsets <- offset_matrix
assays(dds)[['offset']] <- offsets
dds <- DESeq(dds, test='Wald')
# extract interaction contrasts as above

Heavy vs light bin testing

When fraction resolution or depth is limited, pool fractions into light and heavy bins (e.g., 2–5 vs ≥6 ribosomes) based on UV profiles and sequencing depth. Fit an NB GLM with condition×bin interaction and contrast Heavy vs Light within or across conditions. This summarizes redistribution while retaining count-model rigor.

Occupancy index comparisons

Complement GLM tests with simple indices:

• Polysome-to-monosome (P/M) ratio: sum of polysome AUC divided by monosome AUC.
• Heavy occupancy: proportion of normalized counts in heavy bins.
• Polysome propensity: proportion in ≥3-ribosome fractions relative to total.

Interpretation: increases in heavy occupancy suggest elevated translational efficiency; decreases signal repression. Use these indices as sanity checks and to aid figure interpretation.

Disclosure: CD Genomics is our product. In practice, CD Genomics supports fraction-level RNA-seq normalization pipelines and GLM-based differential analysis for polysome experiments, providing customizable reporting and data management; for background on library choices and rRNA depletion, see the concise workflow explainer: poly(A) RNA-seq workflow.

Replicates, batches, and correction

Balanced design and processing

Adopt a balanced design: matched replicate numbers per condition, consistent spike-in addition, recorded fraction volumes, and randomized processing order. Pre-register pooling decisions (e.g., which adjacent fractions constitute light/heavy bins) to avoid post hoc bias. Think of it this way: design discipline is your cheapest power boost for downstream models.

Spike-in scaling and ComBat/RUV

After recovery-aware scaling, address batch effects when batches are known or suspected:

• ComBat-seq applies NB-based adjustment while preserving count characteristics; include batch and relevant covariates. See the 2020 method description for details: ComBat-seq—NB batch adjustment.
• ComBat-ref (a recent refinement) selects a reference batch and can improve clustering/variance partitioning; apply cautiously and validate with diagnostics.
• RUVSeq helps remove unwanted variation using control genes or replicate strategies when batch labels are incomplete.

Diagnostics should show batch effects reduced below condition effects on PCA and variance partitioning.

Concordance and PCA diagnostics

• PCA on normalized fraction profiles should cluster replicates by condition rather than by batch.
• Aim for Pearson r ≥ 0.9 on log-CPM per-fraction profiles between replicates before formal testing.
• Plot spike-in recovery and residuals to identify outlier fractions and samples.

QC metrics and thresholds

rRNA proportion and peak resolution

For libraries intended for polysome profiling normalization, target <5–10% rRNA post-depletion; fractions repeatedly exceeding 20–50% merit deeper sequencing or method revision. Monitor peak resolution on UV254 traces (e.g., P/M ratio >1 in actively translating cells) and visually verify 40S/60S/80S separation.

Fraction purity and RNA integrity

Ensure fraction purity by tracking volumes, cross-contamination risks, and extraction efficiency. Use RIN (≥8.0) or DV200 (for degraded/FFPE contexts) to gate library construction quality. Unique alignment rate ≥70% is a practical sequencing quality target.

Replicate agreement criteria

Proceed to redistribution testing when replicate concordance is high: Pearson r ≥ 0.9 on fraction-resolved profiles and consistent percent-density distributions after monosome alignment. Document any exclusions and justify thresholds in the Methods.

Reporting standards and figures

UV traces and fraction barplots

Include baseline-subtracted UV traces with annotated 40S/60S, 80S monosome, and polysome peaks. Provide fraction AUC barplots and percent ribosome density per fraction following alignment.

Differential association plots

Show GLM-based redistribution results: volcano or coefficient plots for interaction contrasts, heavy vs light bin comparisons, and occupancy indices. Use FDR control (e.g., BH) and provide per-gene effect sizes with confidence intervals.

Methods transparency checklist

Report:

• Gradient composition, ultracentrifugation settings, and inhibitors (e.g., cycloheximide).
• Fraction volumes, pooling rules, and extraction protocol.
• Spike-in design (type/amount), expected vs observed spike metrics, and recovery calculation.
• Statistical model specification: design matrices, offsets, contrasts, and multiple-testing procedure.
• Batch correction method (ComBat/RUV) and diagnostics (PCA, replicate concordance).
• QC metrics: rRNA proportion, alignment rates, RIN/DV200, UV resolution.

For background reading on Ribo-seq integration vs RNA-seq, see an accessible comparison: RNA-seq vs ribosome profiling.

Conclusion

Polysome profiling normalization benefits from disciplined UV254 preprocessing, recovery-aware spike-in scaling, and NB GLM designs with interaction contrasts and per-fraction offsets. Combined with balanced replication, targeted batch correction, and explicit QC thresholds, these practices yield reproducible, publication-ready redistribution analyses. Next steps include integrating fraction-level RNA-seq with Ribo-seq for mechanistic resolution and adopting standardized reporting artifacts—baseline-subtracted traces, fraction density barplots, GLM redistribution figures, and a transparent methods checklist—so reviewers can audit assumptions and reproduce results.

Frequently asked questions (FAQ)

• • Which spike-in type and amount do you recommend for fraction-level normalization?

    • Prefer pre-lysis internal controls: cross-species total RNA (e.g., yeast) or a panel of synthetic mRNAs with a range of lengths. Empirically titrate to a spike that yields 1–5% of total mapped reads per fraction for common cell lines; this preserves sequencing capacity while giving robust recovery estimates. Document exact mass/volume and run a pilot to confirm linear recovery across your fraction volumes.

• • What do I do when some fractions show very low spike-in recovery (R_j)?

    • First, inspect extraction logs and electropherograms. If R_j < 0.2 × median(R) persistently, attempt re-extraction if material remains; otherwise flag the fraction. Options: merge it with an adjacent fraction (if biologically defensible), model it but downweight or exclude it from key contrasts, and always report exclusions with recovery metrics and rationale.

• • How should I construct the offset matrix for NB GLMs from spike-in data?

    • Compute R_j = observed_spike_reads / expected_spike_reads per sample×fraction. The offset for sample s, fraction f is log(R_j × fraction_volume_factor) (use log to preserve multiplicative scaling). Supply this offset matrix to edgeR/DESeq2 so the GLM models relative abundances conditional on recovery-scaled library-like factors.

• • When should I pool fractions into light/heavy bins and how to avoid bias?

    • Pool when per-fraction counts are too sparse for stable per-fraction fits or when biological interpretation favors bins. Define bin boundaries a priori using UV254 features (monosome apex, valley points) and keep pooling rules consistent across samples. Avoid post hoc re-binning driven by significant results; if re-binning is necessary, report both pre-registered and exploratory analyses.

• • rRNA proportion is high in key fractions — how should I proceed?

    • If post-depletion rRNA >20% in critical polysome fractions, consider deeper sequencing, reprocessing with a more aggressive depletion protocol, or excluding affected fractions from sensitive analyses. Always report rRNA proportions per fraction; use alignment-based filters to quantify rRNA and check if high rRNA correlates with low spike recovery or poor UV resolution.

• • Spike-in scaling and batch correction give conflicting signals—what order and diagnostics do you recommend?

    • First apply spike-in–based per-fraction scaling (offsets) to account for recovery and volume differences. Then evaluate batch effects on the normalized counts and apply ComBat-seq or RUV if batch remains significant. Diagnose with PCA and variance partitioning before and after each step; if batch correction removes biological signal, revisit covariates or stratify models instead of aggressive correction.

• • What is the minimum number of biological replicates for redistribution testing?

    • Aim for at least three biological replicates per condition as a practical minimum; four or more improves dispersion estimates and power for interaction tests. Power depends on effect size, dispersion, and fraction granularity—simulate expected contrasts when possible. Treat single-replicate studies as exploratory and avoid strong publication claims.

• • Which visualization mistakes should I avoid when reporting fraction-level results?

    • Don't compare raw AUCs across samples without baseline correction and monosome alignment. Avoid plotting unnormalized counts per fraction; always show normalized or offset-applied metrics. When showing bin contrasts, include the underlying UV254 traces and spike-in recovery plots so readers can assess fraction boundaries and technical variation.

• • Any special considerations when integrating polysome fractionation data with Ribo-seq?

    • Coordinate gene identifiers and transcript isoforms between datasets, and align interpretation levels (ribosome occupancy from Ribo-seq vs. distribution across fractions from polysome RNA). Use Ribo-seq periodicity and P-site metrics for codon-level conclusions; rely on fraction-level RNA-seq for distributional shifts. Cross-validate targets (e.g., ΔFW vs. changes in Ribo-seq footprint density) rather than assuming direct one-to-one correspondence.

• • How should I package data and code to maximize reproducibility and reviewability?

    • Provide raw UV254 traces, fraction metadata (volumes, spike amounts, R_j), raw counts, normalized count matrices, offset matrices, and the exact analysis scripts (R session info). Publish code and toy data in a public repository (GitHub/Zenodo) and include a short README that reproduces key figures and QC tables. In Methods, list software versions and model formulas used for GLM contrasts.