RNA Enrichment Strategies for Low-Input Samples
Team Dynamic Matrices | 17 March 2026
Every RNA enrichment kit has a validated input range on its data sheet, and it usually starts at 100 ng or 1 µg. Below that, performance is not specified because it was not tested. Yet a growing share of the samples that matter, liquid biopsies, sorted subpopulations, laser-captured regions, EV cargo, organoid fragments, come in at picograms to a few nanograms. This post is about why enrichment fails at that scale and what to do about it.
Why standard chemistry breaks down at low input
Most enrichment and depletion steps are mass-action processes. Probe hybridisation rate depends on the concentration of both probe and target; at low target concentration, the reaction slows and, within a fixed incubation, does not reach completion. RNase H digestion depends on the hybrid having formed. Bead capture adds a second problem: at low input, a larger fraction of the sample adsorbs non-specifically to tube walls and bead surfaces and is lost in washes. Adiconis et al. (Nature Methods, 2013) benchmarked depletion and library methods on low-input and degraded RNA and found large differences between chemistries that performed identically at standard input.
The consequence is that a kit reporting 95 percent rRNA depletion at 1 µg can leave 30 percent residual rRNA at 1 ng, and that residual consumes a third of your reads.
The three strategies that hold up
1. Choose chemistry that is less concentration-dependent
Two properties matter: capture that does not rely on reaching hybridisation equilibrium in bulk solution, and capture that does not depend on a solid surface with fixed capacity. Sequence-selective phase separation does both. In LASSO, catcher strands on a soluble polymer hybridise to target and the network condenses; the local concentration of catchers in the collapsing network drives capture to completion even when the bulk target concentration is low, and there is no surface for non-specific loss. In our benchmarks riboLASSO maintains its depletion efficiency down to single-nanogram input where enzymatic methods drop.
For library construction, template-switching chemistries (SMART-seq family) handle picogram input by amplifying before any selection step, at the cost of amplification bias.
2. Cut handling steps
Every transfer, bind, wash and elute loses a fixed fraction of material. At 1 µg you do not notice a 10 percent loss per step. At 1 ng, five steps at 10 percent each leave you with 59 percent of what you started with, and the losses are not random; short fragments and low-abundance transcripts go first. Design the workflow for the minimum number of tube changes. Combine steps where the chemistry allows. Use low-binding plastics and add a carrier (glycogen or linear acrylamide) for any precipitation.
3. Validate at your actual input, not the supplier's
Manufacturer performance data is generated at the input that makes the product look best. Before committing a clinical or diagnostic pipeline, run the enrichment at the input you will actually see, in triplicate, with a spike-in control (ERCC or SIRV) so you can measure recovery. Report the number. If the supplier cannot show data at your input, assume it has not been tested.
What we recommend by input range
| Input | Approach |
|---|---|
| Over 100 ng | Any validated depletion; standard library prep |
| 10 to 100 ng | Depletion chemistry validated at 10 ng; low-input library kit; add spike-ins |
| 1 to 10 ng | Sequence-selective capture or depletion that does not depend on enzymatic completion; minimal-transfer workflow; carrier; low-binding plastics |
| Under 1 ng | Amplify first (template-switching), then enrich, or use targeted capture of a predefined panel; single-cell chemistries |
Why this matters clinically
Liquid biopsy, EV and single-cell diagnostics live at the bottom of that table. A method that is excellent at 100 ng and untested at 1 ng is not a validated method for those workflows. Performance can fall off a cliff below a threshold, and the cliff is not on the data sheet.
References
- Adiconis X et al. Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nat Methods 10, 623–629 (2013).
- Picelli S et al. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 9, 171–181 (2014).
- Jiang L et al. Synthetic spike-in standards for RNA-seq experiments. Genome Res 21, 1543–1551 (2011).
- Shanker S et al. Evaluation of commercially available RNA amplification kits for RNA sequencing using very low input amounts of total RNA. J Biomol Tech 26, 4–18 (2015).
- Dynamic Matrices. LASSO: sequence-selective biomolecule isolation by programmable polymer phase separation. Angew Chem Int Ed (2025).
FAQs
Yes, before any clinical or diagnostic use. Standard-input performance does not predict low-input behaviour, and the failure mode is silent: you get a library, it just under-represents what you care about.
Below about 1 ng, amplify first; the depletion step then runs at a workable concentration. Above that, deplete first to avoid amplifying rRNA.
We have validated riboLASSO to low-nanogram input. At picogram input we recommend amplification first, or a targeted capture panel, and we are glad to run a pilot on your samples.