The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

In the cardiovascular system, changes in the oxidative balance can affect many aspects of cellular physiology through redox-signaling. Depending on the magnitude, fluctuations in the cell's production of reactive oxygen and nitrogen species can regulate normal metabolic processes, activate protective mechanisms, or be cytotoxic. Reactive oxygen and nitrogen species can have many effects including the post-translational modification of proteins at critical cysteine (Cys) thiols. A subset can act as redox-switches, which elicit functional effects in response to changes in oxidative state. While the general concepts of redox-signaling have been established, the identity and function of many regulatory switches remains unclear. Characterizing the effects of individual modifications is the key to understanding how the cell interprets oxidative signals under physiological and pathological conditions. Here, we review the various Cys oxidative post-translational modifications (Ox-PTMs) and their ability to function as redox-switches that regulate the cell's response to oxidative stimuli. In addition, we discuss how these modifications have the potential to influence other post-translational modifications' signaling pathways though cross-talk. Finally, we review the growing number of tools being developed to identify and quantify the various Cys Ox-PTMs and how this will advance our understanding of redox-regulation.

The human heart is capable of functioning for decades despite minimal cell turnover or regeneration, suggesting that molecular alterations help sustain heart function with age. However, identification of compensatory remodeling events in the aging heart remains elusive. We present the cardiac proteomes of young and old rhesus monkeys and rats, from which we show that certain age-associated remodeling events within the cardiomyocyte cytoskeleton are highly conserved and beneficial rather than deleterious. Targeted transcriptomic analysis in Drosophila confirmed conservation and implicated vinculin as a unique molecular regulator of cardiac function during aging. Cardiac-restricted vinculin overexpression reinforced the cortical cytoskeleton and enhanced myofilament organization, leading to improved contractility and hemodynamic stress tolerance in healthy and myosin-deficient fly hearts. Moreover, cardiac-specific vinculin overexpression increased median life span by more than 150% in flies. A broad array of potential therapeutic targets and regulators of age-associated modifications, specifically for vinculin, are presented. These findings suggest that the heart has molecular mechanisms to sustain performance and promote longevity, which may be assisted by therapeutic intervention to ameliorate the decline of function in aging patient hearts.

The human arterial proteome can be viewed as a complex network whose architectural features vary considerably as a function of anatomic location and the presence or absence of atherosclerosis. The data suggest important reductions in mitochondrial protein abundance in early atherosclerosis and also identify a subset of plasma proteins that are highly predictive of angiographically defined coronary disease.

There are an estimated 285 million people with visual impairment worldwide, of whom 39 million are blind. The pathogenesis of many eye diseases remains poorly understood. The human eye is currently an emerging proteome that may provide key insight into the biological pathways of disease. We review proteomic investigations of the human eye and present a catalogue of 4842 non-redundant proteins identified in human eye tissues and biofluids to date. We highlight the need to identify new biomarkers for eye diseases using proteomics. Recent advances in proteomics now allow the identification of hundreds to thousands of proteins in tissues and fluids, characterization of various post-translational modifications, and simultaneous quantification of multiple proteins. To facilitate proteomic studies of the eye, the Human Eye Proteome Project (HEPP) was organized in September 2012. The HEPP is one of the most recent components of the Biology/Disease-driven Human Proteome Project (B/D-HPP) whose overarching goal is to support the broad application of state-of-the-art measurements of proteins and proteomes by life scientists studying the molecular mechanisms of biological processes and human disease. The large repertoire of investigative proteomic tools has great potential to transform vision science and enhance understanding of physiology and disease processes that affect sight.

The separation of peptides by reverse phase liquid chromatography (LC) 1 prior to mass spectrometry detection has become an important technique for the in depth analysis of proteomes in data-dependent acquisition (DDA), data-independent acquisition (DIA), and targeted mass spectrometry workflows. In addition, the reproducibility of the retention and subsequent elution of a peptide from a reversed phase (e.g. C18-based) column at a specific time point along the acidic/ organic gradient has led to the application of retention time (RT) as an orthogonal characteristic applicable to confident peptide identification (2, 3), targeted quantitative assay scheduling (4), and MS1-based quantitation (5). In targeted workflows, where an MS1 precursor and associated MS2 fragment masses (i.e. a transition group or peptide assay) are monitored and quantified with high sensitivity, accurate prior knowledge of peptide RT is a critical piece of information used for distinguishing between multiple extracted ion chromatogram (XIC) peaks that may occur along the chromatographic From the ‡Department

Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical–molecular–biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.

Amidst the proteomes of human tissues lie subsets of proteins that are closely involved in conserved pathophysiological processes. Much of biomedical research concerns interrogating disease signature proteins and defining their roles in disease mechanisms. With advances in proteomics technologies, it is now feasible to develop targeted proteomics assays that can accurately quantify protein abundance as well as their post-translational modifications; however, with rapidly accumulating number of studies implicating proteins in diseases, current resources are insufficient to target every protein without judiciously prioritizing the proteins with high significance and impact for assay development. We describe here a data science method to prioritize and expedite assay development on highimpact proteins across research fields by leveraging the biomedical literature record to rank and normalize proteins that are popularly and preferentially published by biomedical researchers. We demonstrate this method by finding priority proteins across six major physiological systems (cardiovascular, cerebral, hepatic, renal, pulmonary, and intestinal). The described method is data-driven and builds upon the collective knowledge of previous publications referenced on PubMed to lend objectivity to target selection. The method and resulting popular protein lists may also be useful for exploring biological processes associated with various physiological systems and research topics, in addition to benefiting ongoing efforts to facilitate the broad translation of proteomics technologies.