Cell Syst Research Articles

Abstract PO5-03-12: Genomic differences of primary triple negative breast cancer in patients younger than 45 years vs. patients older than 45 years of age

Abstract Background: Triple negative breast cancer (TNBC) is often associated with an aggressive clinical course, especially in younger women. We previously demonstrated that there are differences in the genomic spectrum of metastatic TNBC in patients who are ≤45 years and those who are >45 years of age (Vidula et al, SABCS, 2020). Here we explore genomic differences of primary TNBC in patients ≤45 years and patients >45 years to understand variations in the overall number of non-silent mutations and proportion of patients with specific gene mutations. Methods: We obtained filtered mutation calls generated by the Multi-Center Mutation Calling in Multiple Cancers (MC3) working group (Ellrott et al, Cell Syst, 2018), from whole exome sequencing data of 9079 The Cancer Genome Atlas (TCGA) samples across 33 cancer types that had been used for the comprehensive characterization of cancer driver genes and mutations (Bailey et al, Cell, 2018). Receptor subtype and age at diagnosis of 1247 TCGA breast cancer samples were obtained from the UCSC Xena browser (Goldman et al, Nat Biotechnol, 2020). These datasets were combined to identify 119 patients with primary TNBC with harmonized high confidence mutation calls for analysis. We compared the overall number of non-silent mutations between patients ≤45 years vs. >45 years using the Wilcoxon rank sum test. We also compared the proportion of patients with non-silent mutations in genes mutated at >10% frequency between age groups using the Fisher Exact test. Results: Altogether, 25 patients ≤45 years and 94 patients >45 years were identified with primary TNBC. No significant difference in the number of non-silent mutations among patients ≤45 years vs. >45 years was seen (median [range], ≤45 years: 65 [5-228] vs. >45 years: 74 [2-742], Wilcoxon rank sum p=0.57). In total, 35 unique genes were mutated at >10% frequency in either patients ≤45 years (30 genes) or >45 years (7 genes); notably, TP53 mutations were commonly seen in both age groups (≤45 years: 76%; >45 years: 79%, p=0.79) and TTN mutations were also frequently mutated in both cohorts (≤45 years: 20%; >45 years: 27%, p=0.61). Among the 35 genes identified, 12 genes were mutated at significantly different frequencies among patients ≤45 years vs. those >45 years, as depicted in Table 1. Conclusions: Relatively high frequencies of TP53 and TTN mutations were seen in both patients ≤45 years and those >45 years with primary TNBC. However, many genes associated with varying cellular functions including motility, signaling receptors, and growth were more frequently mutated in patients ≤45 years. These results suggest that there are genomic differences of primary TNBC in patients ≤45 years vs. those >45 years. Further research is needed to validate these findings in a larger cohort of primary TNBC since genomic differences may affect tumor biology and clinical behavior. Table 1. Significant Gene Mutation Differences in Primary TNBC Based on Age. Citation Format: Neelima Vidula, Leif Ellisen, Aditya Bardia, Christina Yau. Genomic differences of primary triple negative breast cancer in patients younger than 45 years vs. patients older than 45 years of age [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-03-12.

Imaging Mass Cytometry Reveals a Highly Heterogenous Cell Makeup of the Follicular Lymphoma Tumour Microenvironment

The highly variable clinical course and response to treatment of follicular lymphoma (FL) is strongly influenced by the tumor microenvironment (TME). While both pro- and anti-tumor effects depend on cell-cell interactions within the TME, these mechanisms remain incompletely understood. Bulk (Dave et al. NEJM 2004) and single-cell RNA-seq studies have identified signatures associated with prognosis, e.g., ‘T cell depleted’ microenvironments that are linked with inferior clinical outcomes (Han et al. Blood Cancer Discov. 2022). While such research has shed light on the TME, single-cell studies often lack spatial context and conventional immunohistochemistry is limited by a low number of parameters. Imaging mass cytometry (IMC) allows multiplexed analysis of up to 40 markers in tissue simultaneously, offering a unique opportunity to study the spatial features of single cells within the TME, informing both disease biology and predictors of clinical outcome. To our knowledge, IMC has more commonly been applied to solid malignancies. FL presents unique challenges in this remit, such as its morphologic variability and phenotyping cells within highly structured, dense lymph-node tissue composed of inherent tight-knit microenvironments. To analyse FL tissue by IMC, we first optimized a 31-plex panel to identify key immune and non-immune lineages including tumor cells, T and NK cells, macrophages, fibroblasts, dendritic, endothelial and reticular cells. Additionally, we wished to identify functional states such as memory, effector or regulatory phenotypes in addition to other parameters including activation, exhaustion and proliferation. Formalin-fixed paraffin embedded (FFPE) biopsies from 100 untreated FL patients enrolled on the Gallium trial (NCT01332968) of frontline chemo-immunotherapy, were subjected to IMC. In total, 381 highly multiplexed images were acquired; 13 were excluded due to suboptimal image quality. Following image pre-processing and deep-learning based cell segmentation, we analysed >3.5 million cells across 368 high-dimensional images (mean=9659 cells, SD=4600). To effectively phenotype large numbers of cells, we constructed a workflow incorporating ‘ASTIR’ (Geuenich et al. Cell Syst. 2021) - a publicly available marker-based algorithm - and cell clustering to identify population subsets. We identified 95 cell populations including Lymphoma subsets (variable CD10, CD11c, CD80 expression), CD4+ and CD8+ T cell subsets (memory, naïve, exhausted etc) and a range of non-lymphoid cells including myeloid and stromal cells. Of particular interest, we found a CD4+ CD3+ PD1+ ICOS+ population which are likely T follicular helper cells (Tfh). On average, Tfh-like cells constitute 1.9% of cells per image (SD=3.1%), although we a found considerable range in their presence. Given previous associations with Tfh cells and FL prognosis we are investigating this variability in further detail. In addition, we have identified ~30 samples enriched for CD57+ T cells, a phenotype suggestive of functional senescence. Amongst substantial heterogeneity of identified cell phenotypes (Fig A), we have identified variable sample sets characterised by enrichment or depletion of i) Lymphoma vs. T-cells (Fig B) and ii) Macrophage vs. T-cell subsets, consistent with previous single cell studies. Here, we report the use of IMC to examine the TME of untreated FL, observing marked heterogeneity of cell phenotypes across FL. Future work seeks to examine prognostic associations between the frequency of cell subsets and their spatial interactions, with this clinically annotated dataset derived from the front-line GALLIUM study. This work will provide a detailed characterization of how the spatial context of FL TME influences disease behaviour, response to treatment and survival. Given the high-throughput and high-plex nature of this study, ultimately, we aim to distil this multitude of information into condensed biomarker(s), to provide potential insights into enhancing diagnostic and/or prognostic capabilities. Therefore, we aim to derive meaningful clinical correlates associated to outcome that can be ultimately translated into practice.

Transcriptomic Comparison of Non-Hodgkin Lymphomas in Relapsed/Refractory Versus Newly Diagnosed Patients Using Single FFPE Slides

Background Patients newly diagnosed with non-Hodgkin Lymphomas (NHLs) have been historically treated with B-cell depleting antibodies and chemotherapy. Standard of care (SoC) for patients with refractory or relapsed (R/R) disease has rapidly evolved over the past decade to incorporate chimeric antigen receptor T-cell therapy (CAR T), lenalidomide, antibody-directed chemotherapies and stem cell transplantation. This diverse treatment landscape can transform the tumor microenvironment and drive heterogenous tumor escape mechanisms. Understanding this varied tumor contexture will help inform efficacy results from NHL therapeutic studies. Core needle biopsies are used to sample tumor from R/R NHL patients; the bulk of tissue may be consumed for histopathology testing, leaving insufficient sample for transcriptomic profiling. We have shown that HTG EdgeSeq is useful for single-slide analysis of formalin fixed, paraffin embedded (FFPE) tissues (Loya et. al., JCOASCO Suppl. 2022). Here, we use this technique to compare tumors of R/R and newly diagnosed patients with Diffuse Large B-cell Lymphoma (DLBCL) and Follicular Lymphoma (FL). MethodsSamples and Transcriptomics: NHL tumor samples from newly diagnosed, treatment naïve patients (DLBCL n = 99, FL n = 19) and lymph node controls (n = 18) were sourced commercially. Tumor samples from a Phase 1/2a trial of epcoritamab in R/R patients (NCT03625037) were collected prior to treatment (DLBCL n = 38, FL n = 5). Single FFPE slides were processed using the EdgeSeq platform (19,000 gene HT panel, HTG Molecular). Tumors originated from lymphoid organs, gastrointestinal tract, testes, and the pleural cavity. Immunohistochemistry imaging for CD3, PAX5, CD20 was also performed (CellCarta, Belgium). Bioinformatics: Differential expression analysis was performed using linear models in R. Gene set enrichment analysis (GSEA) was performed with GSVA in R (Hänzelmann et. al., BMC Bioinformatics. 2013) using published signature sets (mSigDB: Liberzon et al., Cell Syst. 2015, XCell: Aran, et. al., Genome Biol. 2017). Results Single 5μm slides were analyzed with an average tissue size of 8mm2 for core needle biopsies and 40mm2 for resections. The number of detected genes was comparable to RNAseq, suggesting the HTG platform provides comprehensive data using core needle biopsies obtained during routine histopathology. We compared DLBCL tumor to normal lymph nodes and derived a gene set that was highly correlated to pathologist derived tumor purity. This quantitative proxy allows automated identification of samples that may contain source organ tissue. We applied differential expression analysis to identify gene, pathway and immune cell signatures contrasting newly diagnosed and R/R DLBCL. Top genes upregulated in R/R DLBCL tumors included B-cell markers as expected, T-cell function (CD25/IL2RA), and an intriguing epigenetic mechanism (YTHDC1; a N6-methyladenosine reader with a role in lymphoma proliferation [Liu et al., Front Onco 2022]). MYC pathway signaling was enriched in R/R tumors; consistent with known mutational risk factors for progression (Bisso et. al., Immunol Rev. 2019). Th2 polarized CD4+ T-cell signature was higher in treatment naïve DLBCL compared to R/R and normal lymph nodes. Fibroblast gene signatures were higher in R/R DLBCL, potentially a result of microenvironment remodeling due to chemotherapy or tumor driven adaptation (Dumontet et. al., Front Immunol. 2021). Comparison of DLBCL to FL was also performed in both R/R and untreated patient cohorts. Immune cell signatures for Tregs and CD4+ naïve T-cells were enriched in FL, whereas M1 macrophage signatures were enriched in DLBCL (findings were similar in both R/R and treatment naïve groups). Conclusions The EdgeSeq platform enabled whole-transcriptome profiling from very limited tissue samples in lymphoma clinical trials. This study highlights the importance of characterizing the transcriptional heterogeneity of NHL in relation to genetic alterations and T-cell immunity. Ongoing analysis with large patient cohorts in epcoritamab clinical trials will add statistical power to uncover associations with International Prognostic Score, Cell of Origin, effects of different treatment combinations, and genomic alterations on clinical outcomes.

RNA‐Seq Analysis Revealed Sex Differences in Pulmonary Microvascular Endothelial Cells in the Response to Hypoxia

Pulmonary hypertension (PH) is characterized by the abnormal elevation of mean pulmonary arterial pressure which may lead to right ventricle (RV) failure and death. PH is classified into five groups, four of them have been reported to have sex dimorphism with females showing better RV functioning than males. The most marked sex dimorphism is observed in Group 1, pulmonary arterial hypertension (PAH), which is 2‐4 times more common in females than in males. It has only recently been discovered that sex chromosomes may play a role in the sex differences in PH that is independent of sex hormones, although the exact mechanisms are still unknown (1). One of the approaches to get insight into the role of the sex chromosomes in vitro is isolated cells, cultured in the absence of sex hormones. The aim of this study was to reveal sex differences in human pulmonary microvascular endothelial cells, using hypoxia as an in vitro model of pulmonary hypertension.Human pulmonary microvascular endothelial cells (HPMEC) from female (n=4) and male (n=3) healthy donors were exposed to hypoxia (1% O2) for 48 hours. RNA from these samples was sequenced on the Illumina NovaSeq 6000. Differentially expressed genes (DEGs) were identified using DESeq2 (Wald test, p(adj.)<0.05) (2). Gene Set Enrichment Analysis (GSEA, Hallmark gene set collection, FDR<0.05) was used to find enriched pathways (3,4).Hypoxia induced changes in the expression of 79 genes in male HPMEC and of 34 genes in female HPMEC compared to normoxic controls. Among them 19 genes were common between the two sexes, 69 genes changed in males only, 24 genes changed in females only in response to hypoxia. GSEA revealed that in both male and female HPMEC hypoxia induced the enrichment of gene sets “Hypoxia”, “Glycolysis”, “Epithelial Mesenchymal Transition”, “MTORC1 Signaling”, and “Myogenesis”. One gene set, “MYC Targets V1”, was changed in opposite directions in the two sexes: it was upregulated in hypoxia in male HPMEC and downregulated in hypoxia in female HPMEC. Comparisons between male and female HPMEC basally (in normoxia) and in the hypoxic conditions revealed that 11 Y‐encoded genes were expressed more highly in male HPMEC than in female HPMEC (DDX3Y, KDM5D, USP9Y, ZFY, EIF1AY, UTY, RPS4Y1, NLGN4Y, PRKY, TXLNGY, TTTY14). Two X‐encoded genes (XIST, RPS4X) were expressed more highly in female HPMEC in normoxia than in male HPMEC, and one X‐coded gene (XIST) was expressed more highly in female HPMEC in hypoxia than in male HPMEC in hypoxia.According to RNA‐seq analysis, sex differences exist in male and female HPMEC. Some of the identified DEGs and pathways changed in response to hypoxia in the opposite directions in male compared to female HPMEC. Sex differences were induced by sex hormone independent mechanisms (i.e. sex chromosomes), since HPMECs were cultured in the absence of sex hormones. Further research is needed to explore the contribution of the genes and pathways reported here in the development of sex differences in PH. Kostyunina, D. S., & McLoughlin, P. (2021). Antioxidants, 10(5):779; Love, M. I., Huber, W., & Anders, S. (2014). Genome Biology, 15(12), 1–21; Subramaniana et al.(2005). PNAS, 102(43), 15545–15550; Liberzon et al. (2015). Cell Syst, 1(6), 417–425.

Individuals have unique but simple antibody profiles

The human immune system is thought to have the capacity to make billions of different antibodies. Despite the vast potential for variations, the set of antibodies circulating at any given time is actually quite simple , a new study reports ( Cell Syst. 2021, DOI: 10.1016/j.cels.2021.08.008 ). Albert J. R. Heck of Utrecht University and coworkers used mass spectrometry to monitor individual profiles of the antigen-binding fragments (Fabs) from immunoglobulin G1 (IgG1) antibodies circulating in human plasma. Antibodies are proteins produced by the immune system to recognize and neutralize invaders like bacteria and viruses. IgG antibodies represent about 75% of the serum antibodies in humans and are associated with the so-called adaptive immune response, which is tailored to specific pathogens encountered; IgG1 is the most abundant subclass. The researchers measured the mass and chromatographic retention times of Fabs from samples collected at multiple time points from two healthy people and

Hybrid reaction-diffusion and clock-and-wavefront model for the arrest of oscillations in the somitogenesis segmentation clock.

The clock and wavefront paradigm is arguably the most widely accepted model for explaining the embryonic process of somitogenesis. According to this model, somitogenesis is based upon the interaction between a genetic oscillator, known as segmentation clock, and a differentiation wavefront, which provides the positional information indicating where each pair of somites is formed. Shortly after the clock and wavefront paradigm was introduced, Meinhardt presented a conceptually different mathematical model for morphogenesis in general, and somitogenesis in particular. Recently, Cotterell et al. [A local, self-organizing reaction-diffusion model can explain somite patterning in embryos, Cell Syst. 1, 257-269 (2015)] rediscovered an equivalent model by systematically enumerating and studying small networks performing segmentation. Cotterell et al. called it a progressive oscillatory reaction-diffusion (PORD) model. In the Meinhardt-PORD model, somitogenesis is driven by short-range interactions and the posterior movement of the front is a local, emergent phenomenon, which is not controlled by global positional information. With this model, it is possible to explain some experimental observations that are incompatible with the clock and wavefront model. However, the Meinhardt-PORD model has some important disadvantages of its own. Namely, it is quite sensitive to fluctuations and depends on very specific initial conditions (which are not biologically realistic). In this work, we propose an equivalent Meinhardt-PORD model and then amend it to couple it with a wavefront consisting of a receding morphogen gradient. By doing so, we get a hybrid model between the Meinhardt-PORD and the clock-and-wavefront ones, which overcomes most of the deficiencies of the two originating models.

Evaluation of Arai etal.: What Can Differences across Daughter Cells Tell Us about Stem Cell Renewal?

One snapshot of the peer review process for "Machine learning of hematopoietic stem cell divisions from paired daughter cell expression profiles reveals effects of aging on self-renewal" (Arai etal., 2020).

Karonudib Has Potent Anti-Tumor Effects in Preclinical Models of B-Cell Lymphoma

Although chemo-immunotherapy has improved survival in B-cell lymphoma patients, refractory and relapsed disease still represents a major challenge, urging for development of new therapeutics. A new approach is to target nucleotide metabolism. Karonudib (TH1579), was developed to inhibit MutT-homologue-1/Nudix hydrolase 1 (MTH1/NUDT1), an enzyme that prevents oxidized nucleotides to be incorporated into DNA. Under normal conditions with low reactive oxygen species (ROS) burden, MTH1 is not essential for cell survival. This contrasts cancer cells which frequently upregulate MTH1 to compensate for increased ROS with corresponding higher oxidized nucleotide levels, and therefore become more susceptible for MTH1 inhibition. Here, our aim was to perform preclinical testing of karonudib in B-cell lymphoma. Using two different gene expression datasets, we demonstrate that NUDT1, the gene encoding MTH1, was highly upregulated in tumor biopsies from patients with diffuse large B-cell lymphoma (DLBCL) and Burkitt's lymphoma as compared to follicular lymphoma and peripheral blood B cells from healthy donors, hence demonstrating a rationale for targeting MTH1 in aggressive B-cell lymphoma. We tested the efficacy of karonudib (0.06-1 µM) in vitro in a range of B-cell lymphoma cell lines using CellTiterGlo and by flow cytometry detection of active caspase-3 and TUNEL to identify apoptotic cells. Karonudib strongly reduced viability in all B-cell lymphoma cell lines tested (n = 12) and induced apoptosis at concentrations well tolerated by peripheral blood B cells from healthy donors. Cell cycle analysis and microscopy revealed that most cells arrested in prometaphase in the presence of karonudib. Failed spindle assembly led to mitotic arrest and subsequent apoptosis. Prometaphase arrest was seen in TP53 mutant as well as in TP53 wild type cell lines, confirming that karonudib induced apoptosis independent of TP53 mutational status. To test the efficacy of karonudib in vivo, we utilized two different lymphoma xenograft models, including an ABC DLBCL patient-derived model. Mice were treated with karonudib (90 mg/kg) or vehicle b.i.d, three times a week and tumor growth was monitored by in vivo imaging system or MR. In both models, karonudib as single agent completely controlled tumor growth, and significantly prolonged survival (p<0.0001, as compared to control mice). The specificity of MTH1 inhibitors has been debated and TH588, the first generation MTH1 inhibitor, was recently shown to bind b-tubulin and induce mitotic arrest in MTH1 knock out cell lines (Patterson et al, Cell Syst 2019). To elucidate the mechanism of karonudib in B-cell lymphoma, we generated MTH1 knock out cells using CRISPR/Cas9, and compared the functional effects of karonudib in these clones with the original lymphoma cells. We demonstrated on-target effect of the inhibitor as the MTH1 knock out clones were less sensitive towards karonudib. However, MTH1 knock out clones exhibited a similar cell cycle arrest as the wild type cells after karonudib treatment. This clearly indicates that karonudib can induce cell cycle arrest independent of MTH1, and hence has a dual mechanism of action. Our preclinical data suggest that karonudib is a promising drug with potential therapeutic use in B-cell lymphoma, and may be particular effective in aggressive lymphoma types. Disclosures Warpman Berglund: Oxcia AB: Other: shareholders; non profit Thomas Helleday Foundation for Medical Research: Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Gad:Oxcia: Other: shareholder; non profit Thomas Helleday Foundation for Medical Research: Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Pham:Oxcia AB: Other: Shareholder. Sanjiv:Oxcia AB: Other: Shareholder; non profit Thomas Helleday Foundation for Medical Research: Membership on an entity's Board of Directors or advisory committees, Patents & Royalties. Helleday:Oxcia AB: Other: Shareholder; non profit Thomas Helleday Foundation for Medical Research: Membership on an entity's Board of Directors or advisory committees, Patents & Royalties.

Dogs race through development

Aging To get a better understanding of epigenetic changes that occur during development and aging, Wang et al. compared lifetime changes in the DNA methylomes of dogs and humans. Changes in methylation of cytosine-guanine dinucleotides in DNA provide an epigenetic clock that corresponds to age in humans and other animals. The authors alleviated some technical difficulties to allow comparison of data from 104 Labrador retrievers with data from humans and mice. The dog methylomes remodeled quickly in early life, reflecting not just life span but also timing of developmental changes. The authors found a strong association of methylation changes in genes that control developmental pathways, indicating that aging is, in some respects, a continuation of development. Cell Syst. 11 , 1 (2020).

Infinitesimal homeostasis in three-node input-output networks.

Homeostasis occurs in a system where an output variable is approximately constant on an interval on variation of an input variable [Formula: see text]. Homeostasis plays an important role in the regulation of biological systems, cf. Ferrell (Cell Syst 2:62-67, 2016), Tang and McMillen (J Theor Biol 408:274-289, 2016), Nijhout et al. (BMC Biol 13:79, 2015), and Nijhout et al. (Wiley Interdiscip Rev Syst Biol Med 11:e1440, 2018). A method for finding homeostasis in mathematical models is given in the control theory literature as points where the derivative of the output variable with respect to [Formula: see text] is identically zero. Such points are called perfect homeostasis or perfect adaptation. Alternatively, Golubitsky and Stewart (J Math Biol 74:387-407, 2017) use an infinitesimal notion of homeostasis (namely, the derivative of the input-output function is zero at an isolated point) to introduce singularity theory into the study of homeostasis. Reed et al. (Bull Math Biol 79(9):1-24, 2017) give two examples of infinitesimal homeostasis in three-node chemical reaction systems: feedforward excitation and substrate inhibition. In this paper we show that there are 13 different three-node networks leading to 78 three-node input-output network configurations, under the assumption that there is one input node, one output node, and they are distinct. The different configurations are based on which node is the input node and which node is the output node. We show nonetheless that there are only three basic mechanisms for three-node input-output networks that lead to infinitesimal homeostasis and we call them structural homeostasis, Haldane homeostasis, and null-degradation homeostasis. Substantial parts of this classification are given in Ma et al. (Cell 138:760-773, 2009) and Ferrell (2016) among others. Our contributions include giving a complete classification using general admissible systems (Golubitsky and Stewart in Bull Am Math Soc 43:305-364, 2006) rather than specific biochemical models, relating the types of infinitesimal homeostasis to the graph theoretic existence of simple paths, and providing the basis to use singularity theory to study higher codimension homeostasis singularities such as the chair singularities introduced in Nijhout and Reed (Integr Comp Biol 54(2):264-275, 2014. https://doi.org/10.1093/icb/icu010) and Nijhout et al. (Math Biosci 257:104-110, 2014). See Golubitsky and Stewart (2017). The first two of these mechanisms are illustrated by feedforward excitation and substrate inhibition. Structural homeostasis occurs only when the network has a feedforward loop as a subnetwork; that is, when there are two distinct simple paths connecting the input node to the output node. Moreover, when the network is just the feedforward loop motif itself, one of the paths must be excitatory and one inhibitory to support infinitesimal homeostasis. Haldane homeostasis occurs when there is a single simple path from the input node to the output node and then only when one of the couplings along this path has strength 0. Null-degradation homeostasis is illustrated by a biochemical example from Ma etal. (2009); this kind of homeostasis can occur only when the degradation constant of the third node is 0. The paper ends with an analysis of Haldane homeostasis infinitesimal chair singularities.

Raman spectroscopic transcriptome

Cell Biology Raman spectroscopy records the vibrational Raman spectra of biomolecules, allowing the determination of chemical species in target samples. Although nondisruptive and label-free, its application in cell imaging is limited because of the complexity of cellular compositions. Kobayashi-Kirschvink et al. described computational approaches to extract key information from cellular Raman spectra in yeast and bacteria. Dimension-reduction methods enabled a direct prediction of transcriptomes from cellular Raman spectra. A better correlation between the Raman spectra and noncoding transcriptome revealed a more linear correspondence of noncoding RNAs with cellular constituent biomolecules. Although it remains unknown if the linkage can be demonstrated at the single-cell level, spectroscopic live-cell omics provides new possibilities to monitor different cellular states. Cell Syst. 7 , 104 (2018).

Aging gracefully, one organ at a time

Aging![Figure][1] Protein abundance and cellular location change as rats age PHOTO: ALAN & SANDY CAREY/SCIENCE SOURCE You're as old as you feel, goes the old adage, but what is aging at the cellular level? On et al. performed a proteomic and transcriptomic analysis of the brains and livers of 6-month-old young rats and 2-year-old rats. More than 400 proteins changed in abundance between young and old animals, and more than 100 other proteins changed their cellular location, phosphorylation state, or splice form. Although some proteins and protein complexes were altered with age at similar rates, most changes were specific to one organ. These differences could be because of the very different life histories and functions of the two organs: Liver cells continually turn over and regenerate, whereas the brain mostly contains terminally differentiated postmitotic cells. Cell Syst. 10.1016/j.cels.2015.08.012 (2015). [1]: pending:yes

Contralateral Suppression of TEOAEs and DPOAEs in Patients with Tinnitus: An Evidence of Olivocochlear System Disfunction?

Background and Aim: The medial olivocochlear (MOC) system contributes to better sound perception and distinction in noisy environments by causing an inhibitory effect on the cochlear hair cell syst ...