Table_1_Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data.xlsx

Multiplexed imaging is a recently developed and powerful single-cell biology research tool. However, it presents new sources of technical noise that are distinct from other types of single-cell data, necessitating new practices for single-cell multiplexed imaging processing and analysis, particularly regarding cell-type identification. Here we created single-cell multiplexed imaging datasets by performing CODEX on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. After cell segmentation, we implemented five different normalization techniques crossed with four unsupervised clustering algorithms, resulting in 20 unique cell-type annotations for the same dataset. We generated two standard annotations: hand-gated cell types and cell types produced by over-clustering with spatial verification. We then compared these annotations at four levels of cell-type granularity. First, increasing cell-type granularity led to decreased labeling accuracy; therefore, subtle phenotype annotations should be avoided at the clustering step. Second, accuracy in cell-type identification varied more with normalization choice than with clustering algorithm. Third, unsupervised clustering better accounted for segmentation noise during cell-type annotation than hand-gating. Fourth, Z-score normalization was generally effective in mitigating the effects of noise from single-cell multiplexed imaging. Variation in cell-type identification will lead to significant differential spatial results such as cellular neighborhood analysis; consequently, we also make recommendations for accurately assigning cell-type labels to CODEX multiplexed imaging.</p

DataSheet_1_Strategies for Accurate Cell Type Identification in CODEX Multiplexed Imaging Data.pdf

Multiplexed imaging is a recently developed and powerful single-cell biology research tool. However, it presents new sources of technical noise that are distinct from other types of single-cell data, necessitating new practices for single-cell multiplexed imaging processing and analysis, particularly regarding cell-type identification. Here we created single-cell multiplexed imaging datasets by performing CODEX on four sections of the human colon (ascending, transverse, descending, and sigmoid) using a panel of 47 oligonucleotide-barcoded antibodies. After cell segmentation, we implemented five different normalization techniques crossed with four unsupervised clustering algorithms, resulting in 20 unique cell-type annotations for the same dataset. We generated two standard annotations: hand-gated cell types and cell types produced by over-clustering with spatial verification. We then compared these annotations at four levels of cell-type granularity. First, increasing cell-type granularity led to decreased labeling accuracy; therefore, subtle phenotype annotations should be avoided at the clustering step. Second, accuracy in cell-type identification varied more with normalization choice than with clustering algorithm. Third, unsupervised clustering better accounted for segmentation noise during cell-type annotation than hand-gating. Fourth, Z-score normalization was generally effective in mitigating the effects of noise from single-cell multiplexed imaging. Variation in cell-type identification will lead to significant differential spatial results such as cellular neighborhood analysis; consequently, we also make recommendations for accurately assigning cell-type labels to CODEX multiplexed imaging.</p

Solubility Phase Diagram of the Ca(NO₃)₂–LiNO₃–H₂O System

Solubility isotherms of the ternary Ca­(NO₃)₂–LiNO₃–H₂O system were elaborately determined at <i>T</i> = (273.15, 298.15, and 323.15 K) by an isothermal equilibrium method, and the results showed that there are two stable solubility branches for the solid phases Ca­(NO₃)₂·4H₂O and LiNO₃·3H₂O at 273.15 K, and four stable solubility isotherms for the solid phases Ca­(NO₃)₂·4H₂O, Ca­(NO₃)₂·3H₂O, LiNO₃·3H₂O, and LiNO₃ at 298.15 K, and solubility data corresponding to solid phases Ca­(NO₃)₂·3H₂O, Ca­(NO₃)₂·2H₂O, and LiNO₃ at 323.15 K. The experimental data were correlated by a modified Brunauer–Emmett–Teller (BET) model to obtain the complete phase diagram of the ternary system over the temperature range from 273 to 373 K. On the basis of the simulated polytherms, an eutectic point Ca­(NO₃)₂·4H₂O + LiNO₃·3H₂O was recognized, and the melting temperature and fusion heat are 290.5 K and 139.8 J·g^–1, respectively, measured by differential scanning calorimetry

Octahedral Ruthenium Complex with Exclusive Metal-Centered Chirality for Highly Effective Asymmetric Catalysis

A novel ruthenium catalyst is introduced which contains solely achiral ligands and acquires its chirality entirely from octahedral centro­chirality. The configurationally stable catalyst is demonstrated to catalyze the alkynyl­ation of trifluoro­methyl ketones with very high enantio­selectivity (up to >99% ee) at low catalyst loadings (down to 0.2 mol%)

Octahedral Ruthenium Complex with Exclusive Metal-Centered Chirality for Highly Effective Asymmetric Catalysis

A novel ruthenium catalyst is introduced which contains solely achiral ligands and acquires its chirality entirely from octahedral centro­chirality. The configurationally stable catalyst is demonstrated to catalyze the alkynyl­ation of trifluoro­methyl ketones with very high enantio­selectivity (up to >99% ee) at low catalyst loadings (down to 0.2 mol%)

Non‑<i>C</i>₂‑Symmetric Chiral-at-Ruthenium Catalyst for Highly Efficient Enantioselective Intramolecular C(sp³)–H Amidation

A new class of chiral ruthenium catalysts is introduced in which ruthenium is cyclometalated by two 7-methyl-1,7-phenanthrolinium heterocycles, resulting in chelating pyridylidene remote N-heterocyclic carbene ligands (rNHCs). The overall chirality results from a stereogenic metal center featuring either a Λ or Δ absolute configuration. This work features the importance of the relative metal-centered stereochemistry. Only the non-C2-symmetric chiral-at-ruthenium complexes display unprecedented catalytic activity for the intramolecular C­(sp3)–H amidation of 1,4,2-dioxazol-5-ones to provide chiral γ-lactams with up to 99:1 er and catalyst loadings down to 0.005 mol % (up to 11 200 TON), while the C2-symmetric diastereomer favors an undesired Curtius-type rearrangement. DFT calculations elucidate the origins of the superior C–H amidation reactivity displayed by the non-C2-symmetric catalysts compared to related C2-symmetric counterparts

Non‑<i>C</i>₂‑Symmetric Chiral-at-Ruthenium Catalyst for Highly Efficient Enantioselective Intramolecular C(sp³)–H Amidation

A new class of chiral ruthenium catalysts is introduced in which ruthenium is cyclometalated by two 7-methyl-1,7-phenanthrolinium heterocycles, resulting in chelating pyridylidene remote N-heterocyclic carbene ligands (rNHCs). The overall chirality results from a stereogenic metal center featuring either a Λ or Δ absolute configuration. This work features the importance of the relative metal-centered stereochemistry. Only the non-C2-symmetric chiral-at-ruthenium complexes display unprecedented catalytic activity for the intramolecular C­(sp3)–H amidation of 1,4,2-dioxazol-5-ones to provide chiral γ-lactams with up to 99:1 er and catalyst loadings down to 0.005 mol % (up to 11 200 TON), while the C2-symmetric diastereomer favors an undesired Curtius-type rearrangement. DFT calculations elucidate the origins of the superior C–H amidation reactivity displayed by the non-C2-symmetric catalysts compared to related C2-symmetric counterparts

Non‑<i>C</i>₂‑Symmetric Chiral-at-Ruthenium Catalyst for Highly Efficient Enantioselective Intramolecular C(sp³)–H Amidation

A new class of chiral ruthenium catalysts is introduced in which ruthenium is cyclometalated by two 7-methyl-1,7-phenanthrolinium heterocycles, resulting in chelating pyridylidene remote N-heterocyclic carbene ligands (rNHCs). The overall chirality results from a stereogenic metal center featuring either a Λ or Δ absolute configuration. This work features the importance of the relative metal-centered stereochemistry. Only the non-C2-symmetric chiral-at-ruthenium complexes display unprecedented catalytic activity for the intramolecular C­(sp3)–H amidation of 1,4,2-dioxazol-5-ones to provide chiral γ-lactams with up to 99:1 er and catalyst loadings down to 0.005 mol % (up to 11 200 TON), while the C2-symmetric diastereomer favors an undesired Curtius-type rearrangement. DFT calculations elucidate the origins of the superior C–H amidation reactivity displayed by the non-C2-symmetric catalysts compared to related C2-symmetric counterparts

Non‑<i>C</i>₂‑Symmetric Chiral-at-Ruthenium Catalyst for Highly Efficient Enantioselective Intramolecular C(sp³)–H Amidation

A new class of chiral ruthenium catalysts is introduced in which ruthenium is cyclometalated by two 7-methyl-1,7-phenanthrolinium heterocycles, resulting in chelating pyridylidene remote N-heterocyclic carbene ligands (rNHCs). The overall chirality results from a stereogenic metal center featuring either a Λ or Δ absolute configuration. This work features the importance of the relative metal-centered stereochemistry. Only the non-C2-symmetric chiral-at-ruthenium complexes display unprecedented catalytic activity for the intramolecular C­(sp3)–H amidation of 1,4,2-dioxazol-5-ones to provide chiral γ-lactams with up to 99:1 er and catalyst loadings down to 0.005 mol % (up to 11 200 TON), while the C2-symmetric diastereomer favors an undesired Curtius-type rearrangement. DFT calculations elucidate the origins of the superior C–H amidation reactivity displayed by the non-C2-symmetric catalysts compared to related C2-symmetric counterparts

Datasheet1_Revealing the impact of lifestyle stressors on the risk of adverse pregnancy outcomes with multitask machine learning.pdf

Psychosocial and stress-related factors (PSFs), defined as internal or external stimuli that induce biological changes, are potentially modifiable factors and accessible targets for interventions that are associated with adverse pregnancy outcomes (APOs). Although individual APOs have been shown to be connected to PSFs, they are biologically interconnected, relatively infrequent, and therefore challenging to model. In this context, multi-task machine learning (MML) is an ideal tool for exploring the interconnectedness of APOs on the one hand and building on joint combinatorial outcomes to increase predictive power on the other hand. Additionally, by integrating single cell immunological profiling of underlying biological processes, the effects of stress-based therapeutics may be measurable, facilitating the development of precision medicine approaches.ObjectivesThe primary objectives were to jointly model multiple APOs and their connection to stress early in pregnancy, and to explore the underlying biology to guide development of accessible and measurable interventions.Materials and MethodsIn a prospective cohort study, PSFs were assessed during the first trimester with an extensive self-filled questionnaire for 200 women. We used MML to simultaneously model, and predict APOs (severe preeclampsia, superimposed preeclampsia, gestational diabetes and early gestational age) as well as several risk factors (BMI, diabetes, hypertension) for these patients based on PSFs. Strongly interrelated stressors were categorized to identify potential therapeutic targets. Furthermore, for a subset of 14 women, we modeled the connection of PSFs to the maternal immune system to APOs by building corresponding ML models based on an extensive single cell immune dataset generated by mass cytometry time of flight (CyTOF).ResultsJointly modeling APOs in a MML setting significantly increased modeling capabilities and yielded a highly predictive integrated model of APOs underscoring their interconnectedness. Most APOs were associated with mental health, life stress, and perceived health risks. Biologically, stressors were associated with specific immune characteristics revolving around CD4/CD8 T cells. Immune characteristics predicted based on stress were in turn found to be associated with APOs.ConclusionsElucidating connections among stress, multiple APOs simultaneously, and immune characteristics has the potential to facilitate the implementation of ML-based, individualized, integrative models of pregnancy in clinical decision making. The modifiable nature of stressors may enable the development of accessible interventions, with success tracked through immune characteristics.</p