Uncovering cancer metabolic signatures by high-content stimulated Raman scattering (SRS) imaging

Cancer is still one of the most serious health problems worldwide and cancer resistance to chemotherapy, the most wildly use therapeutic strategy for cancer, mounts the biggest challenges for current anti-cancer treatment. The unique characteristics of chemo-resistant cancer cell such as metabolic hallmark can largely facilitate surmounting this difficulty by serving as a therapeutic target to fight against chemo-resistance. However, the understanding of cancer metabolism is still limited, partly resulting from the lack of suitable analytic approaches. My dissertation work applied recently developed stimulated Raman scattering (SRS) imaging on cancer cells to uncover their metabolic signatures for the development of more effective cancer therapy. Taking advantage of SRS imaging, we uncovered that cisplatin-resistant cell have increased fatty acid (FA) uptake, accompanied with reduced glucose uptake and lipogenesis. This metabolic reprograming from glucose to FA dependent anabolic and energy metabolism enables us to develop a rapid diagnostic method for cisplatin-resistance and a therapeutic strategy for cisplatin-resistant cancer. Moreover, we used SRS imaging to estimate the ratio of saturated (SFAs) and unsaturated fatty acids (UFAs) in cancer cell and revealed the role of Stearoyl Co-A desaturase 1 (SCD) on maintaining the intracellular balance of SFAs and UFAs. The unbalance SFAs/UFAs leaded to endoplasmic reticulum (ER) stress, presented as stiff and disorganized ER structure in SRS imaging. This ER stress induced cancer cell apoptosis in vitro, suggesting the therapeutic potential of targeting the lipid balance. To further dissect the metabolic features and reprograming in cancer cells, we developed a high-content hyperspectral SRS (h2SRS) imaging approach by introducing sparsity-driven hyperspectral image decomposition to SRS image post-processing. h2SRS can simultaneously map five major biomolecules involving protein, carbohydrate, FA, cholesterol, and nucleic acid at the single cell level, revealing the acute and adapted metabolic reprograming induced by chemotherapy in cancer cells. This approach accelerates the discoveries of new therapeutic targets against chemo-resistance and benefit the exploration of cellular metabolism study

Disentangled Speech Representation Learning Based on Factorized Hierarchical Variational Autoencoder with Self-Supervised Objective

Disentangled representation learning aims to extract explanatory features or factors and retain salient information. Factorized hierarchical variational autoencoder (FHVAE) presents a way to disentangle a speech signal into sequential-level and segmental-level features, which represent speaker identity and speech content information, respectively. As a self-supervised objective, autoregressive predictive coding (APC), on the other hand, has been used in extracting meaningful and transferable speech features for multiple downstream tasks. Inspired by the success of these two representation learning methods, this paper proposes to integrate the APC objective into the FHVAE framework aiming at benefiting from the additional self-supervision target. The main proposed method requires neither more training data nor more computational cost at test time, but obtains improved meaningful representations while maintaining disentanglement. The experiments were conducted on the TIMIT dataset. Results demonstrate that FHVAE equipped with the additional self-supervised objective is able to learn features providing superior performance for tasks including speech recognition and speaker recognition. Furthermore, voice conversion, as one application of disentangled representation learning, has been applied and evaluated. The results show performance similar to baseline of the new framework on voice conversion.Comment: Published in: 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP

Complex Recurrent Variational Autoencoder for Speech Enhancement

Commonly-used methods in speech enhancement are based on short-time fourier transform (STFT) representation, in particular on the magnitude of the STFT. This is because phase is naturally unstructured and intractable, and magnitude has shown more importance in speech enhancement. Nevertheless, phase has shown its significance in some research and cannot be ignored. Complex neural networks, with their inherent advantage, provide a solution for complex spectrogram processing. Complex variational autoencoder (VAE), as an extension of vanilla \acrshort{vae}, has shown positive results in complex spectrogram representation. However, the existing work on complex \acrshort{vae} only uses linear layers and merely applies the model on direct spectra representation. This paper extends the linear complex \acrshort{vae} to a non-linear one. Furthermore, on account of the temporal property of speech signals, a complex recurrent \acrshort{vae} is proposed. The proposed model has been applied on speech enhancement. As far as we know, it is the first time that a complex generative model is applied to speech enhancement. Experiments are based on the TIMIT dataset, while speech intelligibility and speech quality have been evaluated. The results show that, for speech enhancement, the proposed method has better performance on speech intelligibility and comparable performance on speech quality.Comment: submitted to INTERSPEECH 202

Selective methioninase-induced trap of cancer cells in S/G2 phase visualized by FUCCI imaging confers chemosensitivity.

A major impediment to the response of tumors to chemotherapy is that the large majority of cancer cells within a tumor are quiescent in G0/G1, where cancer cells are resistant to chemotherapy. To attempt to solve this problem of quiescent cells in a tumor, cancer cells were treated with recombinant methioninase (rMETase), which selectively traps cancer cells in S/G2. The cell cycle phase of the cancer cells was visualized with the fluorescence ubiquitination-based cell cycle indicator cell cycle indicator (FUCCI). At the time of rMETase-induced S/G2-phase blockage, identified by the cancer cells' green fluorescence by FUCCI imaging, the cancer cells were administered S/G2-dependent chemotherapy drugs, which interact with DNA or block DNA synthesis such as doxorubicin, cisplatin, or 5-fluorouracil. Treatment of cancer cells with drugs only, without rMETase-induced S/G2 phase blockage, led to the majority of the cancer-cell population being blocked in G0/G1 phase, identified by the cancer cells becoming red fluorescent in the FUCCI system. The G0/G1 blocked cells were resistant to the chemotherapy. In contrast, trapping of cancer cells in S/G2 phase by rMETase treatment followed by FUCCI-imaging-guided chemotherapy was highly effective in killing the cancer cells

Temozolomide and Pazopanib Combined with FOLFOX Regressed a Primary Colorectal Cancer in a Patient-derived Orthotopic Xenograft Mouse Model.

PurposeThe goal of the present study was to determine the efficacy of temozolomide (TEM) and pazopanib (PAZ) combined with FOLFOX (oxaliplatin, leucovorin and 5-fluorouracil) on a colorectal cancer patient-derived orthotopic xenograft (PDOX) mouse model.Materials and methodsA colorectal cancer tumor from a patient previously established in non-transgenic nude mice was implanted subcutaneously in transgenic green fluorescence protein (GFP)-expressing nude mice in order to label the tumor stromal cells with GFP. Then labeled tumors were orthotopically implanted into the cecum of nude mice. Mice were randomized into four groups: Group 1, untreated control; group 2, TEM + PAZ; group 3, FOLFOX; group 4, TEM + PAZ plus FOLFOX. Tumor width, length, and mouse body weight were measured weekly. The Fluor Vivo imaging System was used to image the GFP-lableled tumor stromal cells in vivo. H&E staining and immunohistochemical staining were used for histological analysis.ResultsAll three treatments inhibited tumor growth as compared to the untreated control group. The combination of TEM + PAZ + FOLFOX regressed tumor growth significantly more effectively than TEM + PAZ or FOLFOX. Only the combination of TEM + PAZ + FOLFOX group caused a decrease in body weight. PAZ suppressed lymph vessels density in the colorectal cancer PDOX mouse model suggesting inhibition of lymphangiogenesis.ConclusionOur results suggest that the combination of TEM + PAZ + FOLFOX has clinical potential for colorectal cancer patient

Combination treatment with recombinant methioninase enables temozolomide to arrest a BRAF V600E melanoma in a patient-derived orthotopic xenograft (PDOX) mouse model.

An excessive requirement for methionine termed methionine dependence, appears to be a general metabolic defect in cancer. We have previously shown that cancer-cell growth can be selectively arrested by methionine deprivation such as with recombinant methioninase (rMETase). The present study used a previously-established patient-derived orthotopic xenograft (PDOX) nude mouse model of BRAF V600E-mutant melanoma to determine the efficacy of rMETase in combination with a first-line melanoma drug, temozolomide (TEM). In the present study 40 melanoma PDOX mouse models were randomized into four groups of 10 mice each: untreated control (n=10); TEM (25 mg/kg, oral 14 consecutive days, n=10); rMETase (100 units, intraperitoneal 14 consecutive days, n=10); combination TEM + rMETase (TEM: 25 mg/kg, oral rMETase: 100 units, intraperitoneal 14 consecutive days, n=10). All treatments inhibited tumor growth compared to untreated control (TEM: p=0.0081, rMETase: p=0.0037, TEM-rMETase: p=0.0024) on day 14 after initiation. However, the combination therapy of TEM and rMETase was significantly more efficacious than either mono-therapy (TEM: p=0.0051, rMETase: p=0.0051). The present study is the first demonstrating the efficacy of rMETase combination therapy in a PDOX model, suggesting potential clinical development, especially in recalcitrant cancers such as melanoma, where rMETase may enhance first-line therapy

Recombinant methioninase (rMETase) is an effective therapeutic for BRAF-V600E-negative as well as -positive melanoma in patient-derived orthotopic xenograft (PDOX) mouse models.

Melanoma is a recalcitrant disease. Melanoma patients with the BRAF-V600E mutation have been treated with the drug vemurafenib (VEM) which targets this mutation. However, we previously showed that VEM is not very effective against a BRAF-V600E melanoma mutant in a patient-derived orthotopic xenograft (PDOX) model. In contrast, we demonstrated that recombinant methioninase (rMETase) which targets the general metabolic defect in cancer of methionine dependence, was effective against the BRAF-V600E mutant melanoma PDOX model. In the present study, we demonstrate that rMETase is effective against a BRAF-V600E-negative melanoma PDOX which we established. Forty BRAF-V600E-negative melanoma PDOX mouse models were randomized into four groups of 10 mice each: untreated control (n = 10); temozolomide (TEM) (25 mg/kg, p.o., 14 consecutive days, n = 10); rMETase (100 units, i.p., 14 consecutive days, n = 10); TEM + rMETase (TEM: 25 mg/kg, p.o., rMETase: 100 units, i.p., 14 consecutive days, n = 10). All treatments inhibited tumor growth compared to untreated control (TEM: p = 0.0003, rMETase: p = 0.0006, TEM/rMETase: p = 0.0002) on day 14 after initiation. Combination therapy of TEM and rMETase was significantly more effective than either mono-therapy (TEM: p = 0.0113, rMETase: p = 0.0173). The present study shows that TEM combined with rMETase is effective for BRAF-V600E-negative melanoma PDOX similar to the BRAF-V600E-positive mutation melanoma. These results suggest rMETase in combination with first-line chemotherapy can be highly effective in both BRAF-V600E-negative as well as BRAF-V600E-positive melanoma and has clinical potential for this recalcitrant disease

Broadband Radio Spectral Observations of Solar Eclipse on 2008-08-01 and Implications on the Quiet Sun Atmospheric Model

Based on the joint-observations of the radio broadband spectral emissions of solar eclipse on August 1, 2008 at Jiuquan (total eclipse) and Huairou (partial eclipse) at the frequencies of 2.00 -- 5.60 GHz (Jiuquan), 2.60 -- 3.80 GHZ (Chinese solar broadband radiospectrometer, SBRS/Huairou), and 5.20 -- 7.60 GHz (SBRS/Huairou), the authors assemble a successive series of broadband spectrum with a frequency of 2.60 -- 7.60 GHz to observe the solar eclipse synchronously. This is the first attempt to analyze the solar eclipse radio emission under the two telescopes located at different places with broadband frequencies in the periods of total and partial eclipse. With these analyses, the authors made a new semiempirical model of the coronal plasma density of the quiet Sun and made a comparison with the classic models.Comment: 10 pages, 4 figures, published on Sci. China Ser. G, 2009, Vol.52, page 1765-177