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Robust carbon monitoring systems are needed for land managers to assess and mitigate the changing effects of ecosystem stress on western United States forests, where most aboveground carbon is stored in mountainous areas. Atmospheric carbon uptake via gross primary productivity (GPP) is an important indicator of ecosystem function and is particularly relevant to carbon monitoring systems. However, limited ground-based observations in remote areas with complex topography represent a significant challenge for tracking regional-scale GPP. Satellite observations can help bridge these monitoring gaps, but the accuracy of remote sensing methods for inferring GPP is still limited in montane evergreen needleleaf biomes, where (a) photosynthetic activity is largely decoupled from canopy structure and chlorophyll content, and (b) strong heterogeneity in phenology and atmospheric conditions is difficult to resolve in space and time. Using monthly solar-induced chlorophyll fluorescence (SIF) sampled at ∼4 km from the TROPOspheric Monitoring Instrument (TROPOMI), we show that high-resolution satellite-observed SIF followed ecological expectations of seasonal and elevational patterns of GPP across a 3000 m elevation gradient in the Sierra Nevada mountains of California. After accounting for the effects of high reflected radiance in TROPOMI SIF due to snow cover, the seasonal and elevational patterns of SIF were well correlated with GPP estimates from a machine-learning model (FLUXCOM) and a land surface model (CLM5.0-SP), outperforming other spectral vegetation indices. Differences in the seasonality of TROPOMI SIF and GPP estimates were likely attributed to misrepresentation of moisture limitation and winter photosynthetic activity in FLUXCOM and CLM5.0 respectively, as indicated by discrepancies with GPP derived from eddy covariance observations in the southern Sierra Nevada. These results suggest that satellite-observed SIF can serve as a useful diagnostic and constraint to improve upon estimates of GPP toward multiscale carbon monitoring systems in montane, evergreen conifer biomes at regional scales.

 

Evergreen needleleaf forests (ENFs) play a sizable role in the global carbon cycle, but the biological and physical controls on ENF carbon cycle feedback loops are poorly understood and difficult to measure. To address this challenge, a growing appreciation for the stress physiology of photosynthesis has inspired emerging techniques designed to detect ENF photosynthetic activity with optical signals. This Overview summarizes how fundamental plant biological and biophysical processes control the fate of photons from leaf to globe, ultimately enabling remote estimates of ENF photosynthesis. We demonstrate this using data across four ENF sites spanning a broad range of environmental conditions and link leaf- and stand-scale observations of photosynthesis (i.e., needle biochemistry and flux towers) with tower- and satellite-based remote sensing. The multidisciplinary nature of this work can serve as a model for the coordination and integration of observations made at multiple scales.

 

Abstract Photosynthesis of terrestrial ecosystems in the Arctic-Boreal region is a critical part of the global carbon cycle. Solar-induced chlorophyll Fluorescence (SIF), a promising proxy for photosynthesis with physiological insight, has been used to track gross primary production (GPP) at regional scales. Recent studies have constructed empirical relationships between SIF and eddy covariance-derived GPP as a first step to predicting global GPP. However, high latitudes pose two specific challenges: (a) Unique plant species and land cover types in the Arctic–Boreal region are not included in the generalized SIF-GPP relationship from lower latitudes, and (b) the complex terrain and sub-pixel land cover further complicate the interpretation of the SIF-GPP relationship. In this study, we focused on the Arctic-Boreal vulnerability experiment (ABoVE) domain and evaluated the empirical relationships between SIF for high latitudes from the TROPOspheric Monitoring Instrument (TROPOMI) and a state-of-the-art machine learning GPP product (FluxCom). For the first time, we report the regression slope, linear correlation coefficient, and the goodness of the fit of SIF-GPP relationships for Arctic-Boreal land cover types with extensive spatial coverage. We found several potential issues specific to the Arctic-Boreal region that should be considered: (a) unrealistically high FluxCom GPP due to the presence of snow and water at the subpixel scale; (b) changing biomass distribution and SIF-GPP relationship along elevational gradients, and (c) limited perspective and misrepresentation of heterogeneous land cover across spatial resolutions. Taken together, our results will help improve the estimation of GPP using SIF in terrestrial biosphere models and cope with model-data uncertainties in the Arctic-Boreal region. 

In the anaerobic ergothioneine biosynthetic pathway, a rhodanese domain-containing enzyme (EanB) activates the hercynine’s sp2 ε-C–H bond and replaces it with a C–S bond to produce ergothioneine. The key intermediate for this trans-sulfuration reaction is the Cys412 persulfide. Substitution of the EanB-Cys412 persulfide with a Cys412 perselenide does not yield the selenium analogue of ergothioneine, selenoneine. However, in a deuterated buffer, perselenide-modified EanB catalyzes the deuterium exchange between hercynine’s sp2 ε-C–H bond and D2O. Results from quantum mechanics/molecular mechanics calculations suggest that the reaction involves a carbene intermediate and that Tyr353 plays a key role. We hypothesize that modulating the pKa of Tyr353 will affect the deuterium exchange rate. Indeed, the 3,5-difluoro tyrosine-containing EanB catalyzes the deuterium exchange reaction with a kex ∼10-fold greater than the wild-type EanB (EanBWT). With regard to potential mechanisms, these results support the involvement of a carbene intermediate in the EanB catalysis, rendering EanB as one of the few carbene intermediate-involving enzymatic systems. 

Profiling circulating tumour cells (CTCs) in cancer patients' blood samples is critical to understand the complex and dynamic nature of metastasis. This task is challenged by the fact that CTCs are not only extremely rare in circulation but also highly heterogeneous in their molecular programs and cellular functions. Here we report a combinational approach for the simultaneous biochemical and functional phenotyping of patient-derived CTCs, using an integrated inertial ferrohydrodynamic cell separation (i 2 FCS) method and a single-cell microfluidic migration assay. This combinatorial approach offers unique capability to profile CTCs on the basis of their surface expression and migratory characteristics. We achieve this using the i 2 FCS method that successfully processes whole blood samples in a tumor cell marker and size agnostic manner. The i 2 FCS method enables an ultrahigh blood sample processing throughput of up to 2 × 10 5 cells s −1 with a blood sample flow rate of 60 mL h −1 . Its short processing time (10 minutes for a 10 mL sample), together with a close-to-complete CTC recovery (99.70% recovery rate) and a low WBC contamination (4.07-log depletion rate by removing 99.992% of leukocytes), results in adequate and functional CTCs for subsequent studies in the single-cell migration device. For the first time, we employ this new approach to query CTCs with single-cell resolution in accordance with their expression of phenotypic surface markers and migration properties, revealing the dynamic phenotypes and the existence of a high-motility subpopulation of CTCs in blood samples from metastatic lung cancer patients. This method could be adopted to study the biological and clinical value of invasive CTC phenotypes. 

Rapid and label-free separation of target cells from biological samples provided unique opportunity for disease diagnostics and treatment. However, even with advanced technologies for cell separation, the limited throughput, high cost and low separation resolution still prevented their utility in separating cells with well-defined physical features from a large volume of biological samples. Here we described an ultrahigh-throughput microfluidic technology, termed as inertial-ferrohydrodynamic cell separation (inertial-FCS), that rapidly sorted through over 60 milliliters of samples at a throughput of 100 000 cells per second in a label-free manner, differentiating the cells based on their physical diameter difference with ∼1–2 μm separation resolution. Through the integration of inertial focusing and ferrohydrodynamic separation, we demonstrated that the resulting inertial-FCS devices could separate viable and expandable circulating tumor cells from cancer patients' blood with a high recovery rate and high purity. We also showed that the devices could enrich lymphocytes directly from white blood cells based on their physical morphology without any labeling steps. This label-free method could address the needs of high throughput and high resolution cell separation in circulating tumor cell research and adoptive cell transfer immunotherapy.