The lack of efficient tools to image non-repetitive genes in living cells has limited our ability to explore the functional impact of the spatiotemporal dynamics of such genes. Here, we addressed this issue by developing a CRISPR-Tag system using one to four highly active sgRNAs to specifically label protein-coding genes with a high signal-to-noise ratio for visualization by wide-field fluorescence microscopy. Our approach involves assembling a CRISPR-Tag within the intron region of a fluorescent protein and then integrating this cassette to N- or C-terminus of a specific gene, which enables simultaneous real-time imaging of protein and DNA of human protein-coding genes, such as HIST2H2BE, LMNA and HSPA8 in living cells. This CRISPR-Tag system, with a minimal size of ~250 bp DNA tag, represents an easily and broadly applicable technique to study the spatiotemporal organization of genomic elements in living cells.

Autophagy is a catabolic process through which damaged organelles and protein aggregates are delivered to lysosomes for degradation. Autophagy genes are reported to promote exposure of "eat me" signals on the surface of apoptotic cells, but whether they function in engulfing cells is not clear. Recently, we found that the autophagy mutants atg-18 and epg-5 are defective in removing apoptotic cells derived from the C. elegans Q neuroblast, a phenotype that can be fully rescued by expression of ATG-18 and EPG-5 in the engulfing cell. Loss of ATG-18 or EPG-5 does not affect cell corpse engulfment but causes defects in phagosomal recruitment of RAB-5 and RAB-7 and formation of phagolysosomes. EPG-5, ATG-18 and LGG-1 are sequentially recruited to phagosomes, suggesting that they function at different steps of phagosomal maturation. Our studies indicate that autophagy genes function sequentially to promote apoptotic cell corpse degradation in the engulfing cell.

Purpose

In intensity-modulated proton therapy (IMPT), unaccounted-for variation in biological effectiveness contributes to the discrepancy between the constant relative biological effectiveness (RBE) model prediction and experimental observation. It is desirable to incorporate biological doses in treatment planning to improve modeling accuracy and consequently achieve a higher therapeutic ratio. This study addresses this demand by developing a method to incorporate linear energy transfer (LET) into beam orientation optimization (BOO).

Methods

Instead of RBE-weighted dose, this LET weighted BOO (LETwBOO) framework uses the dose and LET product (LET  ×  D) as the biological surrogate. The problem is formulated with a physical dose fidelity term, a LET  ×  D constraint term, and a group sparsity term. The LET  ×  D of organs at risks is penalized for minimizing the biological effect while maintaining the physical dose objectives. Group sparsity is used to reduce the number of active beams from 600-800 non-coplanar candidate beams to between 2 and 4. This LETwBOO method was tested on three skull base tumor (SBT) patients and three bilateral head-and-neck (H&N) patients. The LETwBOO plans were compared with IMPT plans using manually selected beams with only physical dose constraint (MAN) and the initial MAN plan reoptimized with additional LET  ×  D constraint (LETwMAN).

Results

The LETwBOO plans show superior physical dose and LET  ×  D sparing. On average, the [mean, maximal] doses of organs at risks (OARs) in LETwBOO are reduced by [2.85, 4.6] GyRBE from the MAN plans in the SBT cases and reduced by [0.9, 2.5] GyRBE in the H&N cases, while LETwMAN is comparable to MAN. cLET × Ds of PTVs are comparable in LETwBOO and LETwMAN, where c is a scaling factor of 0.04 μm/keV. On average, in the SBT cases, LETwBOO reduces the OAR [mean, maximal] cLET × D by [1.1, 2.9] Gy from the MAN plans, compared to the reduction by LETwMAN from MAN of [0.7, 1.7] Gy. In the H&N cases, LETwBOO reduces the OAR [mean, maximal] cLET × D by [0.8, 2.6] Gy from the MAN plans, compared to the reduction by LETwMAN from MAN of [0.3, 1.2] Gy.

Conclusion

We developed a novel LET weighted BOO method for IMPT to generate plans with improved physical and biological OAR sparing compared with the plans unaccounted for biological effects from BOO.

Purpose

Spot-scanning proton arc therapy (SPAT) is an emerging modality to improve plan conformality and delivery efficiency. A greedy and heuristic method is proposed in the existing SPAT algorithm to select energy layers and sequence energy switching with gantry rotation, which does not promise optimality in either dosimetry or efficiency. We aim to develop a method to solve the energy layer switching and dosimetry optimization problems in an integrated framework for SPAT.

Methods

In an integrated approach, energy layer optimization for spot-scanning proton arc therapy (ELO-SPAT) is formulated with a dose fidelity term, a group sparsity regularization, a log barrier regularization, and an energy sequencing (ES) penalty. The combination of L2,1/2-norm group sparsity regularization and log barrier function allows one energy layer being selected per control point. The ES regularization term sorts the delivery sequence from high energy to low energy to reduce the total energy layer switching time (ELST) and subsequently the total delivery time. Within the ES penalty, the gradient of layer weights between adjacent beams is first calculated along beam direction and then along energy direction. The gradients indicate energy switch patterns between two adjacent beams. The time-wise costly energy switch-up is more heavily penalized in the ES term. This ELO-SPAT method was tested on one frontal base-of-skull (BOS) patient, one chordoma (CHDM) patient with a simultaneous integrated boost, one bilateral head-and-neck (H&N) patient, and one lung (LNG) patient. We compared ELO-SPAT with intensity-modulated proton therapy (IMPT) using discrete beams and SPArc by Ding et al. For the two arc algorithms, both the plans with and without energy sequencing were created and compared.

Results

Energy layer optimization for spot-scanning proton arc therapy reduced the runtime of optimization by 84% on average compared with the greedy SPArc method. In both the ELO-SPAT plans with and without ES, one energy layer per control point was selected. Without ES regularization, the energy sequence was arbitrary, with around 40-60 switch-up for the tested cases. After adding ES regularization, the number of energy switch-up was reduced to less than 20. Compared with the energy sequenced SPArc plans, the ELO-SPAT plans with ES led to 24% less total ELST for synchrotron plans and 14% less for cyclotron plans. Both the ELO-SPAT and SPArc plans achieved better sparing compared with the IMPT plans for most Organs-at-risks (OARs), with or without ES. Without ES, the ELO-SPAT plans achieved further improvement of the OARs compared with the SPArc plans, with an averaged reduction of OAR [Dmean, Dmax] by [1.57, 3.34] GyRBE. Adding the ES regularization degraded the plan quality, but the ELO-SPAT plans still had comparable or slightly better sparing than the SPArc plans with ES, with an averaged reduction of OAR [Dmean, Dmax] by [1.42, 2.34] GyRBE.

Conclusion

We developed a computationally efficient spot-scanning proton arc optimization method, which solved energy layer selection and sequencing in an integrated framework, generating plans with good dosimetry and high delivery efficiency.

Formation of elaborately branched dendrites is necessary for the proper input and connectivity of many sensory neurons. Previous studies have revealed that dendritic growth relies heavily on ER-to-Golgi transport, Golgi outposts and endocytic recycling. How new membrane and associated cargo is delivered from the secretory and endosomal compartments to sites of active dendritic growth, however, remains unknown. Using a candidate-based genetic screen in C. elegans, we have identified the small GTPase RAB-10 as a key regulator of membrane trafficking during dendrite morphogenesis. Loss of rab-10 severely reduced proximal dendritic arborization in the multi-dendritic PVD neuron. RAB-10 acts cell-autonomously in the PVD neuron and localizes to the Golgi and early endosomes. Loss of function mutations of the exocyst complex components exoc-8 and sec-8, which regulate tethering, docking and fusion of transport vesicles at the plasma membrane, also caused proximal dendritic arborization defects and led to the accumulation of intracellular RAB-10 vesicles. In rab-10 and exoc-8 mutants, the trans-membrane proteins DMA-1 and HPO-30, which promote PVD dendrite stabilization and branching, no longer localized strongly to the proximal dendritic membranes and instead were sequestered within intracellular vesicles. Together these results suggest a crucial role for the Rab10 GTPase and the exocyst complex in controlling membrane transport from the secretory and/or endosomal compartments that is required for dendritic growth.

Purpose

Dose conformality and robustness are equally important in intensity modulated proton therapy (IMPT). Despite the obvious implication of beam orientation on both dosimetry and robustness, an automated, robust beam orientation optimization algorithm has not been incorporated due to the problem complexity and paramount computational challenge. In this study, we developed a novel IMPT framework that integrates robust beam orientation optimization (BOO) and robust fluence map optimization (FMO) in a unified framework.

Methods

The unified framework is formulated to include a dose fidelity term, a heterogeneity-weighted group sparsity term, and a sensitivity regularization term. The L2, 1/2-norm group sparsity is used to reduce the number of active beams from the initial 1162 evenly distributed noncoplanar candidate beams, to between two and four. A heterogeneity index, which evaluates the lateral tissue heterogeneity of a beam, is used to weigh the group sparsity term. With this index, beams more resilient to setup uncertainties are encouraged. There is a symbiotic relationship between the heterogeneity index and the sensitivity regularization; the integrated optimization framework further improves beam robustness against both range and setup uncertainties. This Sensitivity regularization and Heterogeneity weighting based BOO and FMO framework (SHBOO-FMO) was tested on two skull-base tumor (SBT) patients and two bilateral head-and-neck (H&N) patients. The conventional CTV-based optimized plans (Conv) with SHBOO-FMO beams (SHBOO-Conv) and manual beams (MAN-Conv) were compared to investigate the beam robustness of the proposed method. The dosimetry and robustness of SHBOO-FMO plan were compared against the manual beam plan with CTV-based voxel-wise worst-case scenario approach (MAN-WC).

Results

With SHBOO-FMO method, the beams with superior range robustness over manual beams were selected while the setup robustness was maintained or improved. On average, the lowest [D95%, V95%, V100%] of CTV were increased from [93.85%, 91.06%, 70.64%] in MAN-Conv plans, to [98.62%, 98.61%, 96.17%] in SHBOO-Conv plans with range uncertainties. With setup uncertainties, the average lowest [D98%, D95%, V95%, V100%] of CTV were increased from [92.06%, 94.83%, 94.31%, 78.93%] in MAN-Conv plans, to [93.54%, 96.61%, 97.01%, 91.98%] in SHBOO-Conv plans. Compared with the MAN-WC plans, the final SHBOO-FMO plans achieved comparable plan robustness and better OAR sparing, with an average reduction of [Dmean, Dmax] of [6.31, 6.55] GyRBE for the SBT cases and [1.89, 5.08] GyRBE for the H&N cases from the MAN-WC plans.

Conclusion

We developed a novel method to integrate robust BOO and robust FMO into IMPT optimization for a unified solution of both BOO and FMO, generating plans with superior dosimetry and good robustness.

Ligand receptor interactions instruct axon guidance during development. How dendrites are guided to specific targets is less understood. The C. elegans PVD sensory neuron innervates muscle-skin interface with its elaborate dendritic branches. Here, we found that LECT-2, the ortholog of leukocyte cell-derived chemotaxin-2 (LECT2), is secreted from the muscles and required for muscle innervation by PVD. Mosaic analyses showed that LECT-2 acted locally to guide the growth of terminal branches. Ectopic expression of LECT-2 from seam cells is sufficient to redirect the PVD dendrites onto seam cells. LECT-2 functions in a multi-protein receptor-ligand complex that also contains two transmembrane ligands on the skin, SAX-7/L1CAM and MNR-1, and the neuronal transmembrane receptor DMA-1. LECT-2 greatly enhances the binding between SAX-7, MNR-1 and DMA-1. The activation of DMA-1 strictly requires all three ligands, which establishes a combinatorial code to precisely target and pattern dendritic arbors.

Purpose

Proton dose distribution is sensitive to uncertainties in range estimation and patient positioning. Currently, the proton robustness is managed by worst-case scenario optimization methods, which are computationally inefficient. To overcome these challenges, we develop a novel intensity-modulated proton therapy (IMPT) optimization method that integrates dose fidelity with a sensitivity term that describes dose perturbation as the result of range and positioning uncertainties.

Methods

In the integrated optimization framework, the optimization cost function is formulated to include two terms: a dose fidelity term and a robustness term penalizing the inner product of the scanning spot sensitivity and intensity. The sensitivity of an IMPT scanning spot to perturbations is defined as the dose distribution variation induced by range and positioning errors. To evaluate the sensitivity, the spatial gradient of the dose distribution of a specific spot is first calculated. The spot sensitivity is then determined by the total absolute value of the directional gradients of all affected voxels. The fast iterative shrinkage-thresholding algorithm is used to solve the optimization problem. This method was tested on three skull base tumor (SBT) patients and three bilateral head-and-neck (H&N) patients. The proposed sensitivity-regularized method (SenR) was implemented on both clinic target volume (CTV) and planning target volume (PTV). They were compared with conventional PTV-based optimization method (Conv) and CTV-based voxel-wise worst-case scenario optimization approach (WC).

Results

Under the nominal condition without uncertainties, the three methods achieved similar CTV dose coverage, while the CTV-based SenR approach better spared organs at risks (OARs) compared with the WC approach, with an average reduction of [Dmean, Dmax] of [4.72, 3.38]  GyRBE for the SBT cases and [2.54, 3.33] GyRBE for the H&N cases. The OAR sparing of the PTV-based SenR method was comparable with the WC method. The WC method, and SenR approaches all improved the plan robustness from the conventional PTV-based method. On average, under range uncertainties, the lowest [D95%, V95%, V100%] of CTV were increased from [93.75%, 88.47%, 47.37%] in the Conv method, to [99.28%, 99.51%, 86.64%] in the WC method, [97.71%, 97.85%, 81.65%] in the SenR-CTV method and [98.77%, 99.30%, 85.12%] in the SenR-PTV method, respectively. Under setup uncertainties, the average lowest [D95%, V95%, V100%] of CTV were increased from [95.35%, 94.92%, 65.12%] in the Conv method, to [99.43%, 99.63%, 87.12%] in the WC method, [96.97%, 97.13%, 77.86%] in the SenR-CTV method, and [98.21%, 98.34%, 83.88%] in the SenR-PTV method, respectively. The runtime of the SenR optimization is eight times shorter than that of the voxel-wise worst-case method.

Conclusion

We developed a novel computationally efficient robust optimization method for IMPT. The robustness is calculated as the spot sensitivity to both range and shift perturbations. The dose fidelity term is then regularized by the sensitivity term for the flexibility and trade-off between the dosimetry and the robustness. In the stress test, SenR is more resilient to unexpected uncertainties. These advantages in combination with its fast computation time make it a viable candidate for clinical IMPT planning.

Programmed cell death protein-1 (PD-1)/programmed death ligand-1 (PD-L1) immune checkpoint blockade as a breakthrough in cancer immunotherapy has shown unprecedented positive outcomes in the clinic. However, the overall effectiveness of PD-L1 antibody is less than expected. An increasing number of studies have demonstrated that PD-L1 is widely distributed and expressed not only on the cell membrane but also on the inside of the cells as well as on the extracellular vesicles secreted by tumour cells. Both endogenous and exogenous PD-L1 play significant roles in influencing the therapeutic effect of anti-tumour immunity. Herein, we mainly focused on the distribution and function of PD-L1 and further summarized the potential targeted therapeutic strategies. More importantly, in addition to taking the overall expression abundance of PD-L1 as a predictive indicator for selecting corresponding PD-1/PD-L1 monoclonal antibodies (mAbs), we also proposed that personalized combination therapies based on the different distribution of PD-L1 are worth attention to achieve more efficient and effective therapeutic outcomes in cancer patients. LINKED ARTICLES: This article is part of a themed issue on Cancer Microenvironment and Pharmacological Interventions. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.2/issuetoc.

Proper morphogenesis of dendrites plays a fundamental role in the establishment of neural circuits. The molecular mechanism by which dendrites grow highly complex branches is not well understood. Here, using the Caenorhabditis elegans PVD neuron, we demonstrate that high-order dendritic branching requires actin polymerization driven by coordinated interactions between two membrane proteins, DMA-1 and HPO-30, with their cytoplasmic interactors, the RacGEF TIAM-1 and the actin nucleation promotion factor WAVE regulatory complex (WRC). The dendrite branching receptor DMA-1 directly binds to the PDZ domain of TIAM-1, while the claudin-like protein HPO-30 directly interacts with the WRC. On dendrites, DMA-1 and HPO-30 form a receptor-associated signaling complex to bring TIAM-1 and the WRC to close proximity, leading to elevated assembly of F-actin needed to drive high-order dendrite branching. The synergistic activation of F-actin assembly by scaffolding distinct actin regulators might represent a general mechanism in promoting complex dendrite arborization.