Biology subjects

Lee, G.

Publications and source records attributed to Lee, G..

5 recordsLinked to original sources

bioRxiv2018

Single-cell integrative analysis of CAR-T cell activation reveals a predominantly TH1/TH2 mixed response independent of differentiation

We present the first comprehensive portrait of single-cell level transcriptional and cytokine signatures of anti-CD19 4-1BB/CD28/CD3{zeta} CAR-T cells upon antigen-specific stimulation. Both CD4+ helper and CD8+ cytotoxic CAR-T cells are equally effective in directly killing target tumor cells and their cytotoxic activity is associated with the elevation of a range of TH1 and TH2 signature cytokines (e.g., IFN{gamma}, TNF, IL5, and IL13), as confirmed by the expression of master transcription factors TBX21 (T-bet) and GATA3. However, rather than conforming to stringent TH1 or TH2 subtypes, single-cell analysis reveals that the predominant response is a highly mixed TH1/TH2 function in the same cell and the regulatory T cell (Treg) activity, although observed in a small fraction of activated cells, emerges from this hybrid TH1/TH2 population. GM-CSF is produced from the majority of cells regardless of the polarization states, further contrasting CAR-T to classic T cells. Surprisingly, the cytokine response is minimally associated with differentiation status although all major differentiation subsets such as naive, central memory, effector memory and effector are detected. All these suggest that the activation of CAR-engineered T cells is a canonical process that leads to a highly mixed response combining both type 1 and type 2 cytokines together with GMCSF, supporting the notion that polyfunctional CAR-T cells correlate with objective response of patients in clinical trials. This work provides new insights to the mechanism of CAR activation and implies the necessity for cellular function assays to characterize the quality of CAR-T infusion products and monitor therapeutic responses in patients.

bioengineering↗

bioRxiv2018

Abnormal microstructural development of the cerebral cortex in neonates with congenital heart disease is associated with impaired cerebral oxygen delivery

BackgroundAbnormal macrostructural development of the cerebral cortex has been associated with hypoxia in infants with congenital heart disease (CHD). Animal studies have suggested that hypoxia results in cortical dysmaturation at the cellular level. New magnetic resonance imaging (MRI) techniques offer the potential to investigate the relationship between cerebral oxygen delivery and microstructural development of the cortex in newborn infants with CHD.\n\nMethodsWe measured macrostructural and microstructural properties of the cortex in 48 newborn infants with complex CHD and 48 age-matched healthy controls. Cortical volume and gyrification index were calculated from high resolution structural MRI. Neurite density and orientation dispersion indices were modelled using high angular resolution diffusion MRI. Cerebral oxygen delivery was estimated in infants with CHD using phase contrast MRI and pre-ductal pulse oximetry. We used tract-based spatial statistics to examine voxel-wise group differences in cortical microstructure.\n\nResultsMicrostructural development of the cortex was abnormal in 48 infants with CHD, with regions of increased fractional anisotropy (FA) and reduced orientation dispersion index (ODI) compared to 48 healthy controls, correcting for gestational age at birth and scan (FWE-corrected for multiple comparisons at P<0.05). Regions of reduced cortical ODI in infants with CHD were related to impaired cerebral oxygen delivery (R2=0.637, n=39). Cortical ODI was associated with gyrification index (R2=0.589, P<0.0001, n=48).\n\nConclusionsThis study suggests that the primary component of cerebral cortex dysmaturation in CHD is impaired dendritic arborisation, which may underlie abnormal macrostructural findings reported in this population. The degree of impairment was related to cerebral oxygen delivery, supporting the hypothesis that maternal oxygen therapy may be beneficial in this population.

neuroscience↗

bioRxiv2018

Functional repair after ischemic injury through high efficiency in situ astrocyte-to-neuron conversion

Mammalian brains have largely lost internal neural regeneration capability except for a few discrete neurogenic niches. After brain injury, the cerebral cortex is especially difficult to repair due to its extremely low rate of adult neurogenesis. Previous studies have converted glial cells into neurons, but the total number of neurons generated is rather limited, casting doubt about its therapeutic potential. Here, we demonstrate that high-efficiency neuroregeneration can be achieved in adult mammalian brains by making use of an engineered AAV Cre-FLEX system to convert a large number of reactive astrocytes into functional neurons. Specifically, using a combination of GFAP::Cre and FLEX-NeuroD1 AAV system, we were able to regenerate enough new neurons from astrocytes to cover about 40% of the neurons lost from an ischemic injury (400 NeuN+ new neurons/mm2), compared to previously reported an average of <1% of cortical neurons (2-8 NeuN+ neurons/mm2) in an ischemic-injured adult mammalian cortex. Importantly, this in situ astrocyte-to-neuron conversion process also improved survival of injured pre-existing neurons, (additional 400 neurons/mm2), leading to a repaired motor cortex with layered cortical structures. Moreover, NeuroD1-converted neurons not only form functional neural circuits but also rescue motor and memory deficits after ischemic injury. Our results establish the proof-of-principle that a highly efficient in situ astrocyte-to-neuron conversion approach provides a novel treatment for neurological disorders that are in need of new neurons.

neuroscience↗

bioRxiv2018

Reversing Glial Scar Back To Neural Tissue Through NeuroD1-Mediated Astrocyte-To-Neuron Conversion

Nerve injury often causes neuronal loss and glial proliferation, disrupting the delicate balance between neurons and glial cells in the brain. Recently, we have developed an innovative technology to convert internal reactive glial cells into functional neurons inside the mouse brain. Here, we further demonstrate that such glia-to-neuron conversion can rebalance neuron-glia ratio and reverse glial scar back to neural tissue. Specifically, using a severe stab injury model in the mouse cortex, we demonstrated that ectopic expression of NeuroD1 in reactive astrocytes significantly reduced glial reactivity and transformed toxic A1 astrocytes into less harmful astrocytes before neuronal conversion. Importantly, astrocytes were not depleted after neuronal conversion but rather repopulated due to its intrinsic proliferation capability. Remarkably, converting reactive astrocytes into neurons also significantly reduced microglia-mediated neuroinflammation. Moreover, accompanying regeneration of new neurons together with repopulation of new astrocytes, blood-brain-barrier was restored and synaptic density was rescued in the injury sites. Together, these results demonstrate that glial scar can be reversed back to neural tissue through rebalancing neuron:glia ratio after glia-to-neuron conversion.

neuroscience↗

bioRxiv2017

DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest

DNase I hypersensitive sites (DHSs) are genomic regions that provide important information regarding the presence of transcriptional regulatory elements and the state of chromatin. Therefore, identifying DHSs in uncharacterized DNA sequences is crucial for understanding their biological functions and mechanisms. Although many experimental methods have been proposed to identify DHSs, they have proven to be expensive for genome-wide application. Therefore, it is necessary to develop computational methods for DHS prediction. In this study, we proposed a support vector machine (SVM)-based method for predicting DHSs, called DHSpred (DNase I Hypersensitive Site predictor in human DNA sequences), which was trained with 174 optimal features. The optimal combination of features was identified from a large set that included nucleotide composition and di- and trinucleotide physicochemical properties, using a random forest algorithm. DHSpred achieved a Matthews correlation coefficient and accuracy of 0.660 and 0.871, respectively, which were 3% higher than those of control SVM predictors trained with non-optimized features, indicating the efficiency of the feature selection method. Furthermore, the performance of DHSpred was superior to that of state-of-the-art predictors. An online prediction server has been developed to assist the scientific community, and is freely available at: http://www.thegleelab.org/DHSpred.html.

bioinformatics↗