Co-fractionation mass spectrometry (CF-MS) uses biochemical fractionation to isolate and characterize macromolecular complexes from cellular lysates without the need for affinity tagging or capture. In recent years, this has emerged as a powerful technique for elucidating global protein-protein interaction networks in a wide variety of biospecimens. This review highlights the latest advancements in CF-MS experimental workflows including machine learning-guided analyses, for uncovering dynamic and high-resolution protein interaction landscapes with enhanced sensitivity, accuracy and throughput, enabling better biophysical characterization of endogenous protein complexes. By addressing challenges and emergent opportunities in the field, this review underscores the transformative potential of CF-MS in advancing our understanding of functional protein interaction networks in health and disease.

Biochemical fractionation is a technique used to isolate and separate distinct cellular compartments, critical for dissecting cellular mechanisms and molecular pathways. Herein we outline a biochemical fraction methodology for isolation of ultra-pure nuclei and cytoplasm. This protocol utilizes hypotonic lysis buffer to suspend cells, coupled with a calibrated centrifugation strategy, for enhanced separation of cytoplasm from the nuclear fraction. Subsequent purification steps ensure the integrity of the isolated nuclear fraction. Overall, this method facilitates accurate protein localization, essential for functional studies, demonstrating its efficacy in separating cellular compartments. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Biochemical fractionation Support Protocol 1: Protein quantification using Bradford assay Support Protocol 2: SDS/PAGE and Western blotting.

Childhood absence epilepsy seizures arise in the cortico-thalamocortical network due to multiple cellular and molecular mechanisms, which are still under investigation. Understanding the precise mechanisms is imperative given that treatment fails in ~30% of patients while adverse neurological sequelae remain common. Impaired GABAergic neurotransmission is commonly reported in research models investigating these mechanisms. Recently, we reported a region-specific reduction in the whole-tissue and synaptic GABAA receptor (GABAAR) α1 subunit and an increase in whole-tissue GAD65 in the primary somatosensory cortex (SoCx) of the adult epileptic stargazer mouse compared with its non-epileptic (NE) littermate. The current study investigated whether these changes occurred prior to the onset of seizures on postnatal days (PN) 17-18, suggesting a causative role. Synaptic and cytosolic fractions were biochemically isolated from primary SoCx lysates followed by semiquantitative Western blot analyses for GABAAR α1 and GAD65. We found no significant changes in synaptic GABAAR α1 and cytosolic GAD65 in the primary SoCx of the stargazer mice at the critical developmental stages of PN 7-9, 13-15, and 17-18. This indicates that altered levels of GABAAR α1 and GAD65 in adult mice do not directly contribute to the initial onset of absence seizures but are a later consequence of seizure activity.

Absence seizures are hyperexcitations within the cortico-thalamocortical (CTC) network, however the underlying causative mechanisms at the cellular and molecular level are still being elucidated and appear to be multifactorial. Dysfunctional feed-forward inhibition (FFI) is implicated as one cause of absence seizures. Previously, we reported altered excitation onto parvalbumin-positive (PV+) interneurons in the CTC network of the stargazer mouse model of absence epilepsy. In addition, downstream changes in GABAergic neurotransmission have also been identified in this model. Our current study assessed whether dysfunctional FFI affects GABAA receptor (GABAAR) subunit expression in the stargazer primary somatosensory cortex (SoCx). Global tissue expression of GABAAR subunits α1, α3, α4, α5, β2, β3, γ2 and δ were assessed using Western blotting (WB), while biochemically isolated subcellular fractions were assessed for the α and δ subunits. We found significant reductions in tissue and synaptic expression of GABAAR α1, 18% and 12.2%, respectively. However, immunogold-cytochemistry electron microscopy (ICC-EM), conducted to assess GABAAR α1 specifically at synapses between PV+ interneurons and their targets, showed no significant difference. These data demonstrate a loss of phasic GABAAR α1, indicating altered GABAergic inhibition which, coupled with dysfunctional FFI, could be one mechanism contributing to the generation or maintenance of absence seizures.

RNA-binding proteins (RBPs) play essential roles in biology and are frequently associated with human disease. Although recent studies have systematically identified individual RNA-binding proteins, their higher-order assembly into ribonucleoprotein (RNP) complexes has not been systematically investigated. Here, we describe a proteomics method for systematic identification of RNP complexes in human cells. We identify 1,428 protein complexes that associate with RNA, indicating that more than 20% of known human protein complexes contain RNA. To explore the role of RNA in the assembly of each complex, we identify complexes that dissociate, change composition, or form stable protein-only complexes in the absence of RNA. We use our method to systematically identify cell-type-specific RNA-associated proteins in mouse embryonic stem cells and finally, distribute our resource, rna.MAP, in an easy-to-use online interface (rna.proteincomplexes.org). Our system thus provides a methodology for explorations across human tissues, disease states, and throughout all domains of life.

Mass spectrometry (MS) is the core for advanced methods in proteomic experiments. When effectively used, proteomics may provide extensive information about proteins and their post-translational modifications, as well as their interaction partners. However, there are also many problems that one can encounter during a proteomic experiment, including, but not limited to sample preparation, sample fractionation, sample analysis, data analysis & interpretation and biological significance. Here we discuss some of the problems that researchers should be aware of when performing a proteomic experiment.

In order to study the chemical composition of aquatic microbes it is necessary to obtain completely separated fractions of subpopulations. Size separation by filtration is usually unsuccessful because the smaller group of organisms contaminates the larger fractions due to being trapped on filter surfaces of nominally much larger pore sizes. Here we demonstrate that a simple sucrose density separation method allowed us to separate microorganisms of even subtle size differences and to determine their bulk biochemical composition (proteins, polysaccharides+nucleic acids, and lipids). Both autotrophs and heterotrophs (through anaplerotic pathways) were labeled with (14)C-bicarbonate for biochemical fractionation. We provided proof of concept that eukaryotic microbes could be cleanly separated from prokaryotes in cultures and in field samples, enabling detection of differences in their biochemical makeup. We explored methodological issues regarding separation mechanisms, fixation, and pre-concentration via tangential flow filtration of oligotrophic marine waters where abundances of microorganisms are comparably low. By selecting an appropriate centrifugal force, two processes (i.e., isopycnal and rate-zonal separation) can be exploited simultaneously resulting in finely-separated density fractions, which also resulted in size separation. Future applications of this method include exploration of the stoichiometric, biochemical and genetic differences among subpopulations of microbes in a wide variety of aquatic environments.

Composting wastes permits the reuse of organic matter (OM) as agricultural amendments. The fate of OM during composting and the subsequent degradation of composts in soils largely depend on waste OM quality. The proposed study aimed at developing a model to predict the evolution in organic matter quality during the aerobic degradation of organic waste, based on the quantification of the various OM fractions contained in the wastes. The model was calibrated from data gathered during the monitoring of four organic wastes (two non-treated wastes and their digestates) exposed to respirometric tests. The model was successfully fitted for all four wastes and permitted to predict respiration kinetics, expressed as CO2 production rates, and the evolution of OM fractions. The calibrated model demonstrated that hydrolysis rates of OM fractions were similar for all four wastes whereas the parameters related to microbial activity (eg. growth and death rates) were specific to each substrate. These later parameters have been estimated by calibration on respirometric data, thus demonstrating that coupling analyses of OM fractions in initial wastes and respirometric tests permit the simulation of the biodegradation of various type of waste. The biodegradation model presented in this paper could thereafter be integrated in a composting model by implementing mass and heat balance equations.