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Abstract

Illumina-based next generation sequencing (NGS) has accelerated biomedical discovery through its ability to generate thousands of gigabases of sequencing output per run at a fraction of the time and cost of conventional technologies. The process typically involves four basic steps: library preparation, cluster generation, sequencing, and data analysis. In 2015, a new chemistry of cluster generation was introduced in the newer Illumina machines (HiSeq 3000/4000/X Ten) called exclusion amplification (ExAmp), which was a fundamental shift from the earlier method of random cluster generation by bridge amplification on a non-patterned flow cell. The ExAmp chemistry, in conjunction with patterned flow cells containing nanowells at fixed locations, increases cluster density on the flow cell, thereby reducing the cost per run. It also increases sequence read quality, especially for longer read lengths (up to 150 base pairs). This advance has been widely adopted for genome sequencing because greater sequencing depth can be achieved for lower cost without compromising the quality of longer reads. We show that this promising chemistry is problematic, however, when multiplexing samples. We discovered that up to 5-10% of sequencing reads (or signals) are incorrectly assigned from a given sample to other samples in a multiplexed pool. We provide evidence that this \"spreading-of-signals\" arises from low levels of free index primers present in the pool. These index primers can prime pooled library fragments at random via complementary 3 ends, and get extended by DNA polymerase, creating a new library molecule with a new index before binding to the patterned flow cell to generate a cluster for sequencing. This causes the resulting read from that cluster to be assigned to a different sample, causing the spread of signals within multiplexed samples. We show that low levels of free index primers persist after the most common library purification procedure recommended by Illumina, and that the amount of signal spreading among samples is proportional to the level of free index primer present in the library pool. This artifact causes homogenization and misclassification of cells in single cell RNA-seq experiments. Therefore, all data generated in this way must now be carefully re-examined to ensure that \"spreading-of-signals\" has not compromised data analysis and conclusions. Re-sequencing samples using an older technology that uses conventional bridge amplification for cluster generation, or improved library cleanup strategies to remove free index primers, can minimize or eliminate this signal spreading artifact.

Abstract summary

Claude Opus 5.5 · xhigh effort · 18 Oct 2026 · from the abstract

On patterned flow cells using exclusion amplification, up to 5-10% of reads are credited to the wrong sample in a pool, traced to free index primers surviving library cleanup.

Exclusion amplification (ExAmp) with patterned flow cells was adopted widely because it packs clusters more densely, cuts cost per run and holds read quality at longer lengths. The same chemistry misassigns reads between multiplexed samples, so pooled datasets, single-cell RNA-seq among them, may carry signal that belongs to another sample.

Key results

  1. ShownReads from one sample are assigned to other samples sharing the multiplexed pool, at rates reaching 5-10% of reads.

    Reported as up to 5-10% of sequencing reads or signals incorrectly assigned; the abstract states no pool size, library type, index design or replicate count. · Abstract

  2. SuggestedThe cause offered is leftover free index primer: it anneals to pooled fragments through complementary 3' ends, gets extended by polymerase, and the new molecule carries a different index into a cluster.

    Such primers are reported to survive the purification Illumina commonly recommends, and the extent of spreading is said to scale with how much free primer the pool holds; no concentrations or correlation statistics are given. · Abstract

  3. ClaimedIn single-cell RNA-seq the artifact makes profiles look more alike and misassigns cells, which the authors treat as grounds to re-examine data generated this way; older bridge-amplification instruments or stricter cleanup are proposed as fixes.

    Stated in the abstract without an example dataset, a quantified effect on clustering, or a before-and-after comparison of a cleanup protocol. · Abstract

Methods, data and reproducibility

Multiplexed library pools were sequenced with exclusion amplification chemistry on patterned flow cells and read assignment between samples was examined; the residual free index primer left by the standard purification was related to how much signal crossed between samples, with single-cell RNA-seq used to illustrate the consequences.

  • Multiplexed library pools sequenced with exclusion amplification chemistry, including single cell RNA-seq experiments
Methods & tools
Illumina HiSeq 3000/4000/X Ten, exclusion amplification (ExAmp) chemistry, single cell RNA-seq
Reproducibility gaps
No accession, cleanup protocol or analysis code for the sequencing runs appears in the abstract.
Limitations (2)
  • The 5-10% figure is an upper range with no pool composition, index set, library type or replicate count attached, so how it varies between protocols cannot be judged. · noted in this summary
  • The downstream effect on single-cell RNA-seq, and the proposed remedies, are asserted without a quantified example or a corrected re-run reported here. · noted in this summary

Read this if you multiplex libraries on patterned-flow-cell Illumina instruments and need to judge how much signal leaks between pooled samples and which cleanup limits it.

Citations
209

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