Sequential Carryover in LC-MS: A Hidden Threat to Quantitative Accuracy

Liquid chromatography-mass spectrometry (LC-MS) is the workhorse of modern bioanalysis, used to quantify drugs, metabolites, and biomarkers at trace concentrations in complex matrices like plasma and urine. But even a well-validated LC-MS method can be undermined by a subtle and often underestimated problem: sequential carryover (SCO).

What Is Sequential Carryover?

Carryover occurs when residual analyte from one injection persists in the system — in the autosampler, injection needle, tubing, valves, or column — and elutes into a subsequent injection, artificially inflating its measured signal. "Sequential" carryover specifically describes how this contamination can propagate across a run: a single high-concentration sample (for example, one near the upper limit of quantitation, or ULOQ) can leave residue that bleeds into the next sample, and sometimes the one after that, gradually diminishing but not necessarily disappearing after just one blank injection.

This matters because bioanalytical runs are typically structured as long, unattended sequences — calibration standards, quality controls, and unknown study samples injected one after another. If a high-concentration sample sits just before a low-concentration or blank sample, carryover can produce a false positive, an inflated concentration reading, or a result that appears to violate expected pharmacokinetic behavior. In a clinical or regulatory setting, this isn't just an analytical nuisance — it can lead to incorrect dosing decisions, flawed pharmacokinetic modeling, or failed method validation.

Where Carryover Comes From

Carryover is often assumed to be purely an autosampler problem, but the reality is more complicated. Contributing sources include:

• Autosampler components — injection needles, seals, and sample loops where analyte physically adsorbs or is retained between injections

• Tubing and fittings — especially in systems with metal surfaces, where certain analytes (notably basic or chelating compounds) can adsorb non-specifically

• The analytical column itself — column chemistry and particle surface can retain analyte longer than expected, releasing it gradually over subsequent runs

• The mass spectrometer's ionization source — residual analyte in the source housing or capillary can contribute to signal in later injections although rare.

  

Jogpethe and colleagues (2022) proposed a systematic framework for tracing carryover back to its origin, noting that the autosampler and ionization source are the two most common primary sources, while column chemistry, tubing, and fittings frequently compound the problem. Their work emphasizes that effective troubleshooting requires isolating each component of the LC-MS system rather than assuming the autosampler is always the culprit.

How Carryover Is Assessed

The traditional regulatory approach to carryover evaluation is straightforward: inject a blank sample immediately after a ULOQ sample, and confirm that any resulting peak is less than 20% of the lower limit of quantitation (LLOQ) response, and less than 5% of the internal standard response. This pass/fail threshold, borrowed from bioanalytical method validation guidance, has become the industry standard.

But this single-blank check has a well-documented limitation: it doesn't capture how carryover behaves across a full run, particularly when high and low samples are interspersed unpredictably. Zeng, Musson, Fisher, and Wang (2006) addressed this directly, proposing a more rigorous evaluation approach that measures carryover's cumulative influence on quantitation across multiple injections rather than relying on a single blank-after-ULOQ check. Their method offers a more realistic picture of how carryover propagates — and by extension, whether a simple pass/fail criterion is sufficient to guarantee data integrity in an actual study run.

Strategies for Minimizing Carryover

Once a source is identified, several mitigation strategies have proven effective in the literature:

Wash protocol optimization. Rather than a single high-organic wash step, cycling the autosampler needle and system through alternating high- and low-organic solvent washes has been shown to be more effective at stripping residual analyte than a continuous high-organic wash alone.

Gradient and column selection. Williams, Donahue, Gao, and Brummel (2012) demonstrated that carryover is influenced as much by the chromatographic gradient and column choice as by autosampler hardware. In their comparison of multiple columns and wash sequences, they found significant variability in carryover between column chemistries, and ultimately developed a short, generalizable LC-MS method — combining specific column, gradient, and wash conditions — that minimized carryover while remaining robust enough for high-throughput drug discovery bioanalysis.

Hardware and materials. Where non-specific adsorption is suspected, switching to more inert or hydrophilic tubing, fittings, and column hardware (including metal-free or bio-inert flow paths) can reduce analyte retention, particularly for basic or metal-chelating compounds.

Sample pre-treatment. For particularly problematic analytes, upstream sample cleanup — such as solid-phase extraction (SPE) — can reduce the concentration of analyte reaching the LC-MS system in the first place, lowering the risk of carryover downstream.

Why This Deserves More Attention

Sequential carryover is easy to overlook because a single blank-after-ULOQ test can pass while carryover still meaningfully distorts data later in a run — particularly in long clinical or discovery sequences where sample order isn't tightly controlled. As Zeng et al. (2006) and the more recent systematic review by Jogpethe et al. (2022) both emphasize, carryover isn't a single fixed property of a method; it's a dynamic behavior that depends on instrument hardware, chromatographic conditions, and the specific physicochemical properties of the analyte. Robust bioanalytical method development should treat carryover assessment as an ongoing diagnostic process, not a one-time validation checkbox.

Key Takeaways

Sequential carryover in LC-MS is a multi-source problem — autosampler, tubing, column, and ionization source can all contribute — and a single pass/fail blank check may understate its real impact on a study run. Combining root-cause diagnostics, optimized wash and gradient protocols, appropriate hardware selection, and, where necessary, sample pre-treatment offers the most reliable path to minimizing carryover and protecting data integrity.

References

1. Zeng, W., Musson, D. G., Fisher, A. L., & Wang, A. Q. (2006). A new approach for evaluating carryover and its influence on quantitation in high-performance liquid chromatography and tandem mass spectrometry assay. Rapid Communications in Mass Spectrometry, 20(4), 635–640. https://doi.org/10.1002/rcm.2353

2. Williams, J. S., Donahue, S. H., Gao, H., & Brummel, C. L. (2012). Universal LC-MS method for minimized carryover in a discovery bioanalytical setting. Bioanalysis, 4(9), 1025–1037. https://doi.org/10.4155/bio.12.76

3. Jogpethe, A., Jadav, T., Rajput, N., Sahu, A. K., Tekade, R. K., & Sengupta, P. (2022). Critical strategies to pinpoint carryover problems in liquid chromatography-mass spectrometry: A systematic direction for their origin identification and mitigation. Microchemical Journal, 179, 107464. https://doi.org/10.1016/j.microc.2022.107464