Differential labeling for quantitative analysis of complex protein mixtures

Abstract

The disclosed subject matter relates to a method of simultaneously identifying and determining the levels of expression of cysteine-containing proteins in normal and perturbed cells, a method for determining peptide expression levels between a first biological sample and a second biological sample, and a method for quantitative proteomic analysis of two or more peptide populations, and compounds and reagents related thereto.

Description

RELATED APPLICATIONS[0001]The present application is a division of, and claims priority to, U.S. application Ser. No. 10 / 212,628, filed Aug. 1, 2002, entitled “DIFFERENTIAL LABELING FOR QUANTITATIVE ANALYSIS OF COMPLEX PROTEIN MIXTURES”, which in turn is a continuation-in-part of U.S. application Ser. No. 10 / 057,789, filed Jan. 25, 2002 and entitled “DIFFERENTIAL LABELING FOR QUANTITATIVE ANALYSIS OF COMPLEX PROTEIN MIXTURES”, which in turn claims priority to U.S. Provisional Application Ser. No. 60 / 305,232 filed Jul. 13, 2001 and entitled “DIFFERENTIAL LABELING FOR QUANTITATIVE ANALYSIS OF COMPLEX PROTEIN MIXTURES”, and to U.S. Provisional Application Ser. No. 60 / 264,576 filed Jan. 26, 2001 and entitled “DIFFERENTIAL LABELING FOR QUANTITATIVE ANALYSIS OF COMPLEX PROTEIN MIXTURES”, all of which are incorporated by reference herein in their entirety including any drawings.BACKGROUND OF THE INVENTION[0002]Genomic technology has advanced to a point at which, in principle, it has become...

AI Technical Summary

Problems solved by technology

The interpretation of the data obtained by these techniques in the context of the structure, control and mechanism of biological systems has been recognized as a considerable challenge.

In particular, it has been extremely difficult to explain the mechanism of biological processes by genomic analysis alone.

At present no protein analytical technology approaches the throughput and level of automation of genomic technology.

However, the sequential manner in which samples are processed limits the sample throughput, the most sensitive methods have been difficult to automate and low abundance proteins, such as regulatory proteins, escape detection without prior enrichment, thus effectively limiting the dynamic range of the technique.

While these approaches accelerate protein identification, the quantities of the analyzed proteins cannot be easily determined, and these methods have not been shown to substantially alleviate the dynamic range problem also encountered by the 2DE / (MS)n approach.

Therefore, low abundance proteins in complex samples are also difficult to analyze by the μLC / MS / MS method without their prior enrichment.

It is therefore apparent that current technologies, while suitable to identify a portion of the components of protein mixtures, are neither capable of measuring the quantity nor the state of activity of the protein in a mixture.

Even improvements of the current approaches are unlikely to advance their performance sufficiently to make routine quantitative and functional proteome analysis a reality.

While these techniques are quite sensitive, they do not necessarily provide chemical separation of products and may, as a result, be difficult to use for assaying several proteins or enzymes simultaneously in a single sample.

Current methods may not distinguish among aberrant expression of different enzymes or their malfunctions which lead to a common set of clinical symptoms.

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