Recent advances in sample preparation and analysis have enabled direct profiling of protein expression in single mammalian cells and other trace samples. Several techniques to prepare and analyze low-input samples employ custom fluidics for nanoliter sample processing and manual sample injection onto a specialized separation column. While being effective, these highly specialized systems require significant expertise to fabricate and operate, which has greatly limited implementation in most proteomic laboratories. Here, we report a fully automated platform termed autoPOTS (automated preparation in one pot for trace samples) that uses only commercially available instrumentation for sample processing and analysis. An unmodified, low-cost commercial robotic pipetting platform was utilized for one-pot sample preparation. We used low-volume 384-well plates and periodically added water or buffer to the microwells to compensate for limited evaporation during sample incubation. Prepared samples were analyzed directly from the well plate with a commercial autosampler that was modified with a 10-port valve for compatibility with 30 μm i.d. nanoLC columns. We used autoPOTS to analyze 1–500 HeLa cells and observed only a moderate reduction in peptide coverage for 150 cells and a 24% reduction in coverage for single cells compared to our previously developed nanoPOTS platform. To evaluate clinical feasibility, we identified an average of 1095 protein groups from ∼130 sorted B or T lymphocytes. We anticipate that the straightforward implementation of autoPOTS will make it an attractive option for low-input and single-cell proteomics in many laboratories.
Alterations in cardiac development lead to embryonic lethality or congenital heart defects, which affect about 1% of all newborns worldwide. The heart is critical for blood circulation to transport oxygen, nutrients, and waste, but proper blood flow is also key to cardiac development. Early heart contractility and blood flow are suggested by multiple studies to be biomechanical factors that regulate cardiovascular development. Therefore, the ability to reconstruct the dynamic patterns of blood flow in the developing embryos is important to understanding the biomechanical regulation of heart development and improved management of congenital heart defects. Toward this goal, optical coherence tomography (OCT) imaging of mouse embryonic cardiodynamics and novel OCT-based functional analysis methods are actively being developed. Here, we present the development of quantitative OCT angiography toward direction-independent, spatially and temporally resolved blood flow analysis in embryonic vascular structures, which could potentially be expanded to the heart. In contrast to adult blood, individual blood cells can be visualized within the embryonic cardiovascular system. Our approach takes advantage of this feature as well as the periodicity of the cardiac cycle. We demonstrate a capability for spatially resolved flow dynamics in embryonic vasculature in relation to the heartbeat phase. Potentially, the presented method can be expanded to 4D (3D + time) quantitative OCT angiography in the beating heart to enable biomechanical studies.
Scientific progress comes as we build upon the work of others. Implicit in this advance is that we have access to and can thoroughly examine the work of others. It is important to recognize that our scholarly work as scientists encompasses not only experimental design and data collection but also our analytical methods. Thus when communicating biology experiments, especially those that utilize molecular omics data, the analysis methods that connect raw data to scientific conclusions must be presented with sufficient clarity that others can reproduce our exact work. Although there are many resources for sharing raw data files, there is currently not a widely utilized method for sharing analysis methods. We present a semistructured pattern for sharing analysis methods that is simple and efficient and can be implemented by individual laboratories using existing software. This pattern requires three types of files in a publicly accessible repository, such as GitHub: (1) data files, (2) a universal I/O script that parses all data files, and (3) analysis scripts creating figures and metrics reported in the manuscript. We suggest additional conventions to improve the readability and provide a template repository for the pattern. Sharing our exact analysis methods as software, in addition to their narrative description in a manuscript, will ensure reproducibility and transparency. Importantly, the pattern we present does not require new infrastructure and can be achieved without advanced computing skills.
Proteomics analyses can be adapted to samples as small as a single cell, however such sample reduction severely limits the number proteins identified and quantified. Multiple software pipelines exist for processing the raw data to quantitative protein tables. For very small sample amounts, the sensitivity of software algorithms can have a significant impact on the final results. We collected whole blood samples for two subjects by venous blood draws. Using antibody guided flow cytometry cell sorting, we separated B and T cell lymphocytes. For two subjects and two different blood collections, five replicates of 145 B cells or T cells were prepared for mass spectrometry for proteomics using the AutoPOTS workflow. Using the raw files from these forty runs, we perform protein quantification with a selection of common tools such as MaxQuant, ProteomeDiscoverer, and FragPipe and compare the number of identified and quantified proteins. In small sample proteomics, the primary objective is obtaining the most quantified proteins. Although each tool has advantages in terms of usability, speed, and sensitivity, our primary concern is sensitivity. At such a low sample input, we observe that the number of quantified proteins increases by 30% using the most sensitive algorithm. For identifying and quantifying proteins from small samples, the sensitivity of the software is the most important factor to consider. In addition to the identification sensitivity of each algorithm, we are exploring optimal parameters for increased protein quantification. Differences between samples appear regardless of the processing tool, showing that using a less sensitive tool will still characterize the trends but with a significant cost to the number of proteins. Using the most sensitive algorithm for small sample proteomics will greatly improve the number of proteins identified and quantified. Citation Format: Michaela A. McCown, Carolyn Allen, Daniel D. Machado, Samuel H. Payne. Comparison of proteomics identification pipelines for lymphocyte characterization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 274.
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