One-STAGE Tip Method for TMT-Based Proteomic Analysis of a Minimal Amount of Cells.

Liquid chromatography-tandem mass spectrometry (LC-MS)-based profiling of proteomes with isobaric tag labeling from low-quantity biological and clinical samples, including needle-core biopsies and laser capture microdissection, has been challenging due to the limited amount and sample loss during preparation. To address this problem, we developed OnM (On-Column from Myers et al. and mPOP)-modified on-column method combining freeze-thaw lysis of mPOP with isobaric tag labeling of On-Column method to minimize sample loss. OnM is a method that processes the sample in one-STAGE tip from cell lysis to tandem mass tag (TMT) labeling without any transfer of the sample. In terms of protein coverage, cellular components, and TMT labeling efficiency, the modified On-Column (or OnM) displayed similar performance to the results from Myers et al. To evaluate the lower-limit processing capability of OnM, we utilized OnM for multiplexing and were able to quantify 301 proteins in a TMT 9-plex with 50 cells per channel. We optimized the method as low as 5 cells per channel in which we identified 51 quantifiable proteins. OnM method is a low-input proteomics method widely applicable and capable of identifying and quantifying proteomes from limited samples, with tools that are readily available in a majority of proteomic laboratories.

Phosphoproteome Profiling Using an Isobaric Carrier without the Need for Phosphoenrichment.

Phosphorylation is a crucial component of cellular signaling cascades. It controls a variety of biological cellular functions, including cell growth and apoptosis. Owing to the low stoichiometry of phosphorylated proteins, the enrichment of phosphopeptides prior to LC-MS/MS is necessary for comprehensive phosphoproteome analysis, and quantitative phosphoproteomic workflows are typically limited by the amount of sample required. To address this issue, we developed an easy-to-establish, widely applicable, and reproducible strategy to increase phosphoproteomic signals from a small amount of sample without a phosphoenrichment step. By exploiting the multiplexing nature of isobaric labeling to generate a merged signal from multiple samples, and using a larger amount of enriched phosphopeptides as a carrier, we were able to increase trace amounts of phosphopeptides in the unpurified sample to an identifiable level and perform quantification using the reporter ion intensity of the isobaric tag. Our results showed that >1400 phosphopeptides were quantified from 250 ng of tryptic peptides prepared from cells. In a proof-of-concept of our strategy, we distinguished three types of lung cancer cell lines based on their quantitative phosphoproteomic data and identified changes in the phosphoproteome induced by drug treatment.

Changes in the distribution of South Korean forest vegetation simulated using thermal gradient indices.

To predict changes in South Korean vegetation distribution, the Warmth Index (WI) and the Minimum Temperature of the Coldest Month Index (MTCI) were used. Historical climate data of the past 30 years, from 1971 to 2000, was obtained from the Korea Meteorological Administration. The Fifth-Generation National Center for Atmospheric Research (NCAR) /Penn State Mesoscale Model (MM5) was used as a source for future climatic data under the A1B scenario from the Special Report on Emission Scenario (SRES) of the Intergovernmental Panel on Climate Change (IPCC). To simulate future vegetation distribution due to climate change, the optimal habitat ranges of Korean tree species were delimited by the thermal gradient indices, such as WI and MTCI. To categorize the Thermal Analogy Groups (TAGs) for the tree species, the WI and MTCI were orthogonally plotted on a two-dimensional grid map. The TAGs were then designated by the analogue composition of tree species belonging to the optimal WI and MTCI ranges. As a result of the clustering process, 22 TAGs were generated to explain the forest vegetation distribution in Korea. The primary change in distribution for these TAGs will likely be in the shrinkage of areas for the TAGs related to Pinus densiflora and P. koraiensis, and in the expansion of the other TAG areas, mainly occupied by evergreen broad-leaved trees, such as Camellia japonica, Cyclobalanopsis glauca, and Schima superba. Using the TAGs to explain the effects of climate change on vegetation distribution on a more regional scale resulted in greater detail than previously used global or continental scale vegetation models.

Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data.

The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging (LiDAR) data. A digital canopy model (DCM), generated from the LiDAR data, was combined with aerial photography for segmenting crowns of individual trees. To eliminate errors in over and under-segmentation, the combined image was smoothed using a Gaussian filtering method. The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method. After measuring the crown area from the segmented individual trees, the individual tree diameter at breast height (DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area. The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute. The carbon storage, based on individual trees, was estimated by simple multiplication using the carbon conversion index (0.5), as suggested in guidelines from the Intergovernmental Panel on Climate Change. The mean carbon storage per individual tree was estimated and then compared with the field-measured value. This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.