Bronchoalveolar lavage is an effective way of collecting samples of cells and molecules from the alveoli of the lungs, the analysis of which may allow proteomic determination of signatures of disease states and so aid diagnosis. Such analysis has, however, been hampered by a number of factors. CLI caught up with Professors Griffin and Wendt from the University of Minnesota, MN, USA, to find out more about their work on developing optimized sample processing workflows to improve quantitative proteomic analysis of these samples.
What is bronchoalveolar lavage fluid (BALF), how is it collected and why is it useful for study?
BALF is collected in the clinic from patients with health conditions of the lung. It is done by passing a bronchoscope through the upper airway to the lower airways of the lung. A saline solution is introduced for washing (also known as lavaging) the distal lung tissue. The lavage fluid is withdrawn by suction, effectively collecting cells and molecules found on the lining of lung tissues.
BALF is rich in cells and molecules sampled from distal lung
tissue, making it a valuable clinical specimen for studies seeking to understand molecular characteristics of lung disease and biology. BALF is made up of individual cells such as alveolar macrophages and other inflammatory cells known to be a part of the immune system response to disease and their associated DNA and RNA. It also contains a variety of biomolecules including proteins, metabolites, and lipids. Proteins are a prominent component of BALF, including those expressed in cells in specific disease pathologies. Because proteins carry out the biochemical reactions in the cell that drive disease mechanisms, profiling the collection of proteins (also known as the proteome) in BALF is of high value to understanding lung and upper airway disease. BALF also contains peptides produced by proteases that cleave larger proteins into shorter amino acid sequences, some of which are involved in immune response and disease.
How have the proteins in BALF been analysed in the past and currently and what are the limitations/challenges of these techniques?
Mass spectrometry (MS)-based proteomics is a versatile technology for detecting and quantifying the complement of proteins present in complex clinical samples such as BALF. Some of the first proteomic studies of BALF used two-dimensional gel electrophoresis to separate and visualize the proteins present in these samples, coupled with digestion of separated proteins with trypsin and analysis using nanoscale liquid chromatography (LC) tandem mass spectrometry (MS/MS). The MS/MS spectra are generated from the fragmentation of detected peptides and used to identify the amino acid sequences making up these peptides using specialized bioinformatic software tools. Once peptides in the sample are identified, they are used to infer the presence of their parent proteins, and also quantify the abundance the proteins when compared between different sample types (e.g. disease versus healthy controls).
Although initial MS-based proteomics studies of BALF showed promise, the depth of these studies were limited by challenges that are inherent to these sample types. These include: (i) the presence of very high abundance proteins, many from human serum that make it difficult to detect lung-tissue proteins that are at much lower abundance; (ii) the presence of lung-specific molecules such as surfactants and salts that are not compatible with LC-MS analysis; and (iii) limiting amounts of starting material that is diluted by BALF collection and which is especially problematic in diseases where only small volumes of BALF can be collected (e.g. studies of lung disease in children). Despite advances in MS-instrumentation that have increased their sensitivity and helped address some of these challenges, recent proteomic studies of BALF have only been demonstrated in samples with relatively large amounts of BALF, in some cases from pooled samples – less desirable if the goal is to characterize individual patient samples collected in larger sample cohorts for clinical studies of disease.
What can be done to overcome these limitations?
Understanding these long-standing challenges in MS-based proteomic analysis of BALF, we set out to develop a more robust sample processing workflow that could offer improvements and be generally applicable to a wide-variety of translational studies of lung and upper airway diseases. Our workflow brings together several steps in an easily accomplished, integrated processing pipeline. This includes a step for depleting high abundance proteins derived from serum that are found in BALF, followed
by a step to trap, concentrate and remove contaminants from remaining proteins in an efficient manner that limits sample losses. The final step employs a method called tandem mass tagging (TMT) which labels each separate protein sample with stable-isotope encoded tags and enables relative abundance measurements of identified proteins. Such quantitative analysis is critical for understanding differences in protein signatures that may associate with disease states.
What does your new workflow enable that couldn’t be achieved before?
We demonstrated the effectiveness of our workflow by analysing BALF from patients diagnosed with chronic obstructive pulmonary disease (COPD). In this work, we demonstrated several unique features of our workflow that have not been achieved before. One feature is amenability of the workflow to BALF samples of both larger starting volumes (≥5 mL) or those with relatively lower volumes (1–5 mL). This makes our workflow applicable to studies where low amounts of starting BALF material may be available (such as studies of pediatric lung disease) as well as studies focused on adults where larger amounts of BALF can be collected. A second feature we demonstrated is the compatibility with either label-free quantification methods using MS analysis, or using stable-isotope labelling methods such as TMT for accurate, relative abundance measurements in large patient cohorts. We conducted experiments to show that the processing workflow is quantitatively repeatable, important to ensure that relative abundance measurements delivered by the method are accurate, whether they be generated using label-free or labelling methods. Finally, our workflow offers a value-added feature of easy collection of naturally occurring, smaller peptides from BALF, that also enables the analysis of these potentially important disease markers in clinical samples.
What do you envisage for the future of BALF analysis in relation to disease diagnosis?
Our workflows for sample processing should be a critical piece to an emerging technological platform that is significantly improving clinical studies of disease using MS-based proteomics. Using this workflow, researchers should be able to identify those proteins found in BALF of highest interest as diagnostic or prognostic markers of disease. Once defined, these proteins can become the focus of MS-based targeted methods that are capable of detecting and quantifying peptides from proteins of interest with high sensitivity and accuracy, and with high throughput. Such targeted assays have been demonstrated for the analysis of larger cohorts of clinical samples. Additionally, a number of new MS-instrumentation platforms have been introduced that have significantly improved sensitivity and analysis speed. These now make it possible to gain proteome-wide data on samples very rapidly (30 minutes or less). These platforms offer the possibility to conduct high-throughput analysis of large-scale clinical sample cohorts (hundreds to thousands of samples) and achieve new discoveries with high statistical power into markers and molecular mechanisms of lung and upper airway disease. We envision our sample processing workflow could be automated and play a key role in this emerging high-throughput quantitative MS-based proteomics pipeline for clinical studies. When applied to the analysis of BALF, this pipeline should lead to new discoveries that will improve diagnosis, treatment and monitoring of lung and airway disease.
For further details, see Weise DO, Kruk ME, Higgins L et al. An optimized workflow for MS-based quantitative proteomics of challenging clinical bronchoalveolar lavage fluid (BALF) samples. Clin Proteomics 2023;20(1):14. doi: 10.1186/s12014-023-09404-1 (https://clinicalproteomicsjournal.biomedcentral.com/articles/10.1186/s12014-023-09404-1)