The LC-MS techniques are commonly used to analyze intact proteoforms for top-down proteomics. To deepen the coverage of the intact human proteome, multidimensional separations are often applied prior to the MS analysis. However, the most common quantitative top-down approach, label-free quantification, cannot be applied to multidimensional separations because proteins that elute in multiple fractions cannot be quantified. The focus of this dissertation is the development of high-throughput quantitative top-down proteomics techniques for the analysis of complex biological samples (e.g., bacteria and human cell lysate) using isobaric chemical tag labeling for application to multidimensional separation and quantitative top-down proteomics. To improve the intact proteome coverage of human protein samples, we applied a 2D pH RP/RPLC-MS platform to characterize intact human proteoforms. The high resolution of RPLC and orthogonality between high-pH and low-pH RPLC separations provide an aerial view of the intact human proteome. Our results demonstrated that the 2DLC platform dramatically improves the identification of intact proteins and proteoforms, and largely enhances the detection of low abundant proteoforms and PTMs. The isobaric chemical tag labeling quantification has been widely applied in bottom-up proteomics. However, protein-level labeling is challenging due to the tendency of intact proteins to aggregate under labeling conditions. We developed a filter-SEC approach to enrich low MW proteins from the complex sample, these proteins maintain solubility in protein-level TMT labeling for quantitative top-down MS analysis. Once the protein aggregation issue had been addressed, however, we found that protein-level TMT labeling under standard labeling conditions resulted in unwanted side products (underlabeled or overlabeled species). This issue is more serious for intact proteoform labeling because a higher number of residues are potentially labeled when compared with shorter peptides. We comprehensively optimized and evaluated the protein-level TMT labeling conditions including TMT to protein ratio, final pH (quenching solution concentration), reaction concentration (starting sample volume), reaction time, and reaction buffers. After optimization of parameters, we found that 85% of the proteoforms were completely labeled after the reaction; this improvement in labeling efficiency allows protein-level isobaric chemical tag labeling to be used for real applications. Overall, our results demonstrate the potential of quantitative top-down proteomics using TMT and multidimensional separation. High throughput quantitative top-down proteomics techniques using protein-level TMT labeling hold great potential for the relative quantification of intact proteoforms. We hope that our work can push the isobaric chemical tag labeling quantification for intact proteoforms into practical applications.