Hi-N Quantitation For Clinical Discovery Proteomics

Progenesis QI for proteomics provides untargeted absolute quantitation of all identified proteins via the Hi-N method. This post explains the method and how it can be a useful tool for discovery proteomics in a clinical setting.

What is Hi-N?

Graph from Silva et al. (2006)Hi-N is a label-free quantitation method allowing absolute quantitation of all identified proteins in a sample, using just a single un-labelled internal standard. Other literature variously describes the method as Top3 or Hi3.

The method relies on a discovery made by Silva et al. (2006) that the average integrated signal intensity of the top 3 most intense tryptic peptides is proportional to the absolute amount of a protein present in a sample. Their graph (right) shows a linear relationship over 2 orders of magnitude for 6 proteins on 6 samples (R2 = 0.9939).

The Hi-N method in Progenesis QI for proteomics chooses, for each protein, the N peptides with the highest abundance and averages their abundances to produce a Hi-N measurement. The number of peptides to consider (N) is configurable, but defaults to 3 as per the majority of literature. By incorporating a known amount of a single internal standard to your samples, the absolute amount of all other proteins can then be calibrated:

Absolute amount of protein A = (Hi-N value for protein A)/(Hi-N value for internal standard) * (Absolute amount of internal standard)

How well does it work?

The principle that the method relies upon (i.e. that the average abundance of the top 3 peptides is proportional to the absolute amount of protein) has been verified by a number of studies, using different instruments and data collection techniques:

The relationship between the average MS signal response of the three most intense tryptic peptides and the absolute quantity of protein can be immediately inferred from the relative ratio of the average MS signal responses. The relative ratios of the average MS signal responses are proportional to the absolute quantities of each protein present in the sample.

Silva et al. (2006) [Waters Q-TOF with LCMSE]

We show that only the Top3 method is directly proportional to protein abundance over the full quantification range and is the preferred method in the absence of reference protein measurements.

Ahrné et al. (2013) [Thermo Orbitrap]

Fig. 2 shows a linearity between the average of the three most intense MS signals of tryptic peptides of one protein and the protein abundance.

Grossman et al. (2010) [Thermo LTQ-FT-ICR]

Further studies have shown the method to have a good dynamic range, high reproducibility and excellent correlation with typical clinical quantitation methods such as routine immunoassays:

The dynamic range of protein abundances spanned four orders of magnitude. The correlation between technical replicates of the ten biological samples was R2 = 0.9961 ± 0.0036 (95% CI = 0.9940 – 0.9992) and the technical CV averaged 7.3 ± 6.7% (95% CI = 6.87 – 7.79%). This represents the most sophisticated label-free profiling of skeletal muscle to date.

Burniston et al. (2014)

One of the key factors required for accurate quantification is high reproducibility of abundance (intensity) measurements. The abundance coefficient of variation (CV) was calculated for all detected peptides in the three data sets (Fig. 6). The average CVs were 0.08 ± 0.1, 0.26 ± 0.09, and 0.18 ± 0.09 for the 4-protein mixture, serum, and tissue data sets, respectively (mean±standard deviation).

Levin et al. (2011)

Our study demonstrates that LCMSE allows reproducible untargeted quantitation of abundant plasma proteins. It gives fair to excellent correlation with immunoassays, and is achieved at low setup costs, without costly isotope-labelled standards used in targeted proteomics approaches. Reasonable variability compared to these targeted-approaches also gives confidence with regard to using this method.

Kramer et al. (2015)

This high correlation with the “gold-standard” of immunoassays suggests that discoveries made using Hi-N will transfer well to further validation studies using targeted methods such as MRM or immunoassays. This makes it a good candidate for quantitation of large numbers of proteins in clinical discovery proteomics.

How do I use it?

By default, Progenesis QI for proteomics provides relative Hi-N values calculated without an internal standard. This provides you with an abundance measure that is proportional to the absolute amount of protein in your samples, without any additional processing or sample preparation steps.

To obtain absolute measurements for all proteins in your experiment, you simply need to add a known amount of internal standard to each sample. Then it’s a simple case of telling Progenesis the accession and amount of your internal standard added. Progenesis will automatically re-calibrate your abundance values to provide absolute measurements (in fmol). So with just the addition of a single internal standard, you get absolute quantitation of all proteins in your sample effectively “for free” – with no additional analysis steps.

Protein quant options in the automatic processing tool Protein quantitation options in Progenesis QI for proteomics

You can configure your internal standard (refered to as “calibrant” in Progenesis QI for proteomics) either at the automatic processing set up wizard, or later on in the workflow when reviewing your identified proteins.

Why should I use it?

The label-free Hi-N method provides quantitative precision similar to labelled methods, without the greater expense, preparation time and variability the labelling process brings. The label-free approach is applicable to any kind of sample, in comparison to some labelled approaches – not all labelling methods are applicable in all scenarios, and in some methods only a subset of proteins are actually labelled.

Quantitative measurements in label-free proteomics have typically only allowed for relative “cross-run” comparison. Such measurements can only be validly compared for a single protein across runs. The linearity of the Hi-N method allows, in addition, comparison between proteins in the same run, providing much more information about the relative amounts of different proteins in your samples.


In conclusion, the Hi-N method provides a useful tool for quantitation in clinical discovery proteomics. The measurements obtained correlate well with routine immunoassays and labelled approaches, so make it likely that discoveries will transfer well to MRM/immunoassay validation studies. The only extra sample preparation required is the addition of a known amount of a single (non-labelled) internal standard to each sample.

Progenesis QI for proteomics performs Hi-N quantitation (without an internal standard) by default. Absolute quantitation using an internal standard is simply a case of entering the standard’s accession and spiked amount. If you’d like to find out more, get in touch, or download Progenesis QI for proteomics and try it for yourself.


Silva, J. C., Gorenstein, M. V., Li, G. Z., Vissers, J. P. C., & Geromanos, S. J. (2006). Absolute quantification of proteins by LCMSE – a virtue of parallel MS acquisition. Molecular & Cellular Proteomics, 5(1), 144-156.

Ahrné, E., Molzahn, L., Glatter, T., & Schmidt, A. (2013). Critical assessment of proteome-wide label-free absolute abundance estimation strategies. Proteomics, 13(17), 2567-2578.

Grossmann, J., Roschitzki, B., Panse, C., Fortes, C., Barkow-Oesterreicher, S., Rutishauser, D., & Schlapbach, R. (2010). Implementation and evaluation of relative and absolute quantification in shotgun proteomics with label-free methods. Journal of proteomics, 73(9), 1740-1746.

Burniston, J. G., Connolly, J., Kainulainen, H., Britton, S. L., & Koch, L. G. (2014). Label-free profiling of skeletal muscle using high-definition mass spectrometry. Proteomics, 14(20), 2339-2344.

Levin, Y., Hradetzky, E., & Bahn, S. (2011). Quantification of proteins using data-independent analysis (MSE) in simple and complex samples: A systematic evaluation. Proteomics, 11(16), 3273-3287.

Kramer, G., Woolerton, Y., van Straalen, J. P., Vissers, J. P. C., Dekker, N., Langridge, J. I., Benyon, R. J., Speijer, D., Sturk, A. & Aerts, J. M. F. G. (2015). Accuracy and Reproducibility in Quantification of Plasma Protein Concentrations by Mass Spectrometry without the Use of Isotopic Standards. PloS one, 10(10), e0140097.