Corporate Overview December 2022 Exhibit 99.1

Forward-looking Statements and Other Information This presentation contains forward-looking statements that involve substantial risks and uncertainties. All statements, other than statements of historical facts, contained in this presentation are forward-looking statements, including statements regarding: the potential of HTG Molecular Diagnostics, Inc. (“HTG”)’s proprietary RNA platform technologies to make the development of life science tools and diagnostics more effective and efficient and to unlock a differentiated and disruptive approach to transformative drug discovery; HTG being positioned for high value-add throughout the drug discovery and companion diagnostic development processes; potential CDx revenues; potential payments from therapeutics business; the ability of HTG’s technology to de-risk drug candidates; the ability of transcriptome profiling to disrupt drug discovery and development; anticipated project timelines; potential value milestones; potential market opportunities and the size of those markets; potential customers; the benefits and capabilities of HTG’s technologies; and HTG’s plans, goals and objectives. The words ‘‘anticipate,’’ ‘‘believe,’’ ‘‘estimate,’’ ‘‘expect,’’ ‘‘intend,’’ ‘‘may,’’ ‘‘plan,’’ ‘‘predict,’’ ‘‘project,’’ ‘‘target,’’ ‘‘potential,’’ ‘‘will,’’ ‘‘would,’’ ‘‘could,’’ ‘‘should,’’ ‘‘continue,’’ and similar expressions are intended to identify forward-looking statements, although not all forward-looking statements contain these identifying words. We may not actually achieve the plans, intentions or expectations disclosed in our forward-looking statements, and you should not place undue reliance on our forward-looking statements. Actual results or events could differ materially from the plans, intentions and expectations disclosed in forward-looking statements, including due to various risks and uncertainties, including risks and uncertainties associated with drug discovery and development; the risk that HTP and our RNA platform and medicinal chemistry technologies may not provide the benefits that we expect; risks associated with our ability to develop and commercialize our products; risks associated with drug discovery and development; risks associated with our ability to successfully out-license or partner assets that we may develop through our drug discovery and development business; illustrative payments or revenue opportunities may be substantially higher than any payments or revenues we ultimately receive, if any; the risk that our products and services may not be adopted by biopharmaceutical companies or other customers as anticipated, or at all; our ability to manufacture our products to meet demand; competition in our industry; additional capital and credit availability; our ability to attract and retain qualified personnel; risks associated with the impact of the COVID-19 pandemic on us and our customers; and product liability claims. These and other factors are discussed under the heading “Risk Factors” in our quarterly report on Form 10-Q for the quarter ended September 30, 2022, as filed with the Securities and Exchange Commission on November 10, 2022. The forward-looking statements contained in this presentation reflect our views with respect to future events as of the date this presentation, and we undertake no obligation to update such statements to reflect events that occur or circumstances that exist after the date on which they were made.   In addition, the information in this presentation should be read together with our Annual Report on Form 10-K for the year ended December 31, 2021, our Quarterly Reports on Form 10-Q for the periods ended March 31, 2022, June 30, 2022 and September 30, 2022, and our Current Reports on Form 8-K filed in 2022, in each case, as filed with the SEC and other than any information that is furnished and not filed.  

PRECISION MEDICINE Platform Assets Platform based pharma partnering Drug Assets Transcriptome-informed small moleculedrug discovery

The Power of RNA and Gene Expression Profiling (GEP) in Precision Medicine RNA Transcriptome DNA Protein Proteome Making PrecisionMedicine More Precise M H S V I D Genome RNA is what is happening Proteins are the working molecules …but are very difficult to multiplex DNA represents the blueprint Mutations are a powerful element of precision medicine but many patientsdo not have an actionable mutation In many disease areas, DNA mutations are not relevant to disease Pathway and gene analysis are excellent biomarkers to understand disease and mechanism of action (MOA) Transcript abundance is often superior to protein analysis, and easierto multiplex Protein analysis represents the gold standard of understanding what is happening biologically Structure and multiplexing are two significant barriers inhibiting protein analysis

Strategy Leverage proprietary technologies to help improve drug discovery/development efficiency and effectiveness Use captive RNA profiling and ML driven chemistry engine to build a pipeline of potentially best in class drug candidates to known targets and advance them into clinical development Selectively partner profiling/chemistry technology engine with strategic stakeholders to expand beyond HTG targets and candidate pipeline

Vidya Kankipati​ VP, Scientific Services Carl Kaub VP, Chemistry John Lubniewski President and Chief Executive Officer Stephen A. Barat SVP, Therapeutics Debrah Thompson VP, Scientific Innovation Christina Carruthers VP, Target Strategy Shaun D. McMeansSenior Vice President and Chief Financial Officer Troy Svihl Patent Counsel Proven Team with Rich, Relevant Expertise Expertise RNA profiling Bioinformatics Medicinal chemistry Molecule design Target/pipeline strategy Licensing strategy

Problems We Address Drug Candidate Attrition Getting the Right Drug to the Right Patient Drug Discovery Preclinical 3-6 Years FDA Review Scale-up to MFG NDA Submitted .5-2 Years 20-100 100-500 1000-5000 6-7 Years Clinical Trial IND Submitted ~5,000 – 10,000 Compounds 250 Pre-Discovery Phase 1 Phase 2 Phase 3 Number of Volunteers 5 One FDA- Approved Drug

HTG Positioned for High Value-add Throughout The Process DRUG DISCOVERY CDx DEVELOPMENT NIH / Academia profiling tools Iterative Process Potential royalties andmilestones or asset sale Objective: Patented development candidateout licensed or soldby HTG CDx Development Target Discovery Validation Library Design Cell-based Screening Hit to Lead 1 2 3 4 5 Pre-IND Phase 1 Phase 2 Phase 3 Launch 1 2 3 4 5 Potential CDx revenue Objective: Companion Diagnostic Development profiling tools

THERAPEUTICS Pioneering a transcriptome-informed approach to drug discovery and design Reducing to practice the use of transcriptomic profiling to understanding cellular perturbation. Our approach is unique and anchored in captive technologies Platform is an integration of captive profiling capabilities and medicinal chemistry platform utilizing a propriety machine-learning driven approach. Improving upon a traditional approach to drug discovery We design and select molecular candidates following a paradigm that we believe will improve the selection and design, thereby de-risking and increasing chances for success in development.

Literature concurs that integrating big data expression profiling can improve therapeutic candidates Early and often transcriptome profiling can potentially disrupt drug development

What Differentiates HTG for Drug Discovery? Functional integration of full RNA profiling, in silico chemistry, and primary data-driven redesign: Potentially de-risked drug candidates FULL TRANSCRIPTOMIC PROFILING INTEGRATED ML DRIVEN CHEMISTRY TRANSCRIPTOME-INFORMED SAR Full mRNA, m6a and miRNA profiling 3 day turn around > 99% success Proprietary cost effective technology Proprietary in-silico-based molecular design Allows for more intelligent design of molecular entities Faster and lower cost than traditional methods Profiling and pathway analysis informs library redesign candidate molecules – an iterative process Real time analysis enables rapid cycling Biological interpretation of transcriptome-informed data set designed to ultimately yield improved molecule design and selection (i.e., de-risked therapeutic candidates)

OUR SOLUTION The Value of Early Pre-clinical Insight Used too latein process Clinical Transcriptomic profiling Few levers: Dosing Post-facto patient selection TODAY Use early,during small moleculediscovery and design Preclinical Full system transcriptomic profiling Medicinalchemistry Many levers: MOA / biology Which patients respond → patient selection Which pathways are activated → off-target effects OPPORTUNITY

Proprietary Profiling Technology HTG Epi-EdgeSeq Epi transcriptome mRNA transcriptome miRNA transcriptome EdgeSeq Platform Technology for Drug DiscoveryHTG’s Tech Enabled Drug Discovery Platform Neurology Cardiovascular Diabetes Liver Disease Oncology/IO Rare Disease Infectious Disease Immunology Genetic Disease Metabolic Disease Disease state agnostic: Precision Therapeutics Patented Molecules Companion Diagnostics and HTG Informatics Informatics HTG Chemistry Machine learning driven small molecule chemistry Proprietary Chemistry Platform IP Strategy

Current Focus Rapid path to clinical POC Clear patient selection strategy Amenable to HTG Platform Innovators Challengers Target is a driver of disease Clear patient selection strategy Unmet Medical Need Exists Favorable Competitive Landscape Clear Patient Selection Strategy Good IP Protection Possible Rapid Path to Patients Criteria For Target Selection

Role of the target in the biology of the given disease. Location/expression of target, liabilities, etc. Preclinical proof of concept Target modulation in pharmacologic or disease model? Is the target druggable? Factors that influence target expression Age, comorbidities, etc. Potential modality Competitive landscape Health Outcomes and Economics CMC considerations Strategic application of our captive transcriptomic technology and machine learning to differentiate our asset over competition Target Strategy Approach – Why we are different

Sequencing Library prep Data Generation: HTG EdgeSeq Sequencer Agnostic Illumina Thermo Fisher Scientific Sample prep Patented andProprietary Chemistry (U.S. Patent No. 8,741,564) Targeted RNA sequencing, 48 hour total time m6A RNA modifications (“epi-transcriptome”) mRNA Transcriptome miRNA Transcriptome Panels

2 HTG Chemistry Platform Synthesis In Vitro/Vivo Testing Normalization and Formatting Property and Attribute Calculation Filtering Evaluation, Prioritization and Iterative Design Data Extraction Model Selection , Training, Scoring, and Structure Generation Papers, patents, commercial monomers, screening compound sets, structures from known literature Utilize experimental data to feedback on design Create custom chemical space and Redock using data to train models and generate IP Calculation of properties and various attributes such as fingerprints and shape and space descriptors. Apply custom filters such as PAINS, REOS Synthesize targeted library biological testing Data preparation, pre-process normalization, deconstruct pharmacophor, generate 3D conformers

Analyze disease vs normal with mRNA, miRNA, m6A and other modifications. Map disease states Identify potential targets Knock on/knock off CRISPR Target validation Profiling Pharmacophore, structure and ligand based structures Binding site assessments In silico screening Targets are screened with targeted libraries to identify hits Transcriptomic profiling and pathway analysis to inform chemistry Structure optimization Continued transcriptomic profiling to inform optimization The enter LO/LLO via further preclinical characterization. Target Discovery Validation Library Design Cell-based Screening Hit to Lead 1 2 3 4 5 A Deeper DiveTranscriptomic Informed Drug Discovery and Design HTG Iterative Process Early Development Candidate HTG Iterative Process

RNA Profiling Internal Experimental Data Data Sources Target Specific Data HTG Drug Discovery Engine Convolutional Neural Network Deep Neural Network LSTM Neural Network Deep Reinforcement Learning Graph Neural Network Convolutional Neural Network Deep Neural Network Engine ML Algorithms Chemical Structure Data Customized Fragment and Compound Spaces Pre-Trained Data Autoencoded Blended Data Experimental Data Candidate Molecules

Proof of Approach Work HTG Therapeutics White Papers Proof of Approach

Compound to Profile Relationships in Discovery An intriguing difference between mTOR inhibitors is the differential regulation of the ferroptosis pathway. Our RNA data show differential regulation of this key pathway across a range of these structurally similar compounds. C03 C02 In the discovery setting, our approach uncovers biologically-relevant compound-to-profile relationships not seen by conventional methods.

Profile to Compound We expect our machine learning based engine technology to allow generation of molecules from desired  RNA profiles.  

IP Strategy – Two Pronged Protecting the “Engine” Patent protection EdgeSeq assay technology 6 issued patents, including US, AU, CA, CN, JP, and EP (GB, FR, IT, DE, ES) m/miRNA codetection assay technology 3 issued patents, including US, AU, and CA EdgeSeq v2.0 assay technology 5 pending applications, including US, AU, CA, BR, and CN 2 issued patents, including JP and EP (FR, GB, DE) Target assay technology 1 pending PCT application Trade Secret protection for aspects of the Engine that are not publicly visible E.g., AI algorithms and methods of their use Protecting the therapeutic candidates Project specific patent protection Claiming the target compounds, methods of using target compounds, and combination therapies that include the target compounds First target provisional applications being prepared

Profiling and Diagnostics Advantaged technology in RNA translational profiling Enables broad application of high quality RNA GEP in translational research Robust technology for real world issues Very sample efficient, high assay pass rates and quick turn around times Highly complementary in drug development opportunity Expected to enable advanced partnering opportunities for OEM and BioPharma Significant opportunity synergistic to drug discovery Built in engine for highly valued companion diagnostics

What Differentiates HTG for RNA Analysis? Faster…easier…higher expected success rate…lower sample requirement SAMPLE FRIENDLY SIMPLIFIED WORKFLOW CUTTING-EDGE PERFORMANCE 5-10x less starting sample required Compatible with both liquid andtissue samples Maintain effectiveness with degraded samples Avoids complex and biased RNA-seq workflow steps Turn-around time of 3 days vs.7 days or more Bioinformatics in <1 hour vs.4-8 hours or more Excellent concordance with RNASeq and synthetic RNA spike in controls 97+% assay success rate and large dynamic range High-plex (up to 20K) panels, including whole transcriptome mRNA and miRNA panels

Momentum in Publications Referencing EdgeSeq

Provides the mRNA transcriptome with the same value proposition as previous EdgeSeq targeted panels Smaller sample load High success rate with FFPE Simpler workflow Faster turnaround time Easy bioinformatics Helping to enable the next round of growth HTG’s new human mRNA transcriptome product, approximately 20,000 genes

Assay comparison and eRNA/FFPE performance vs RNASeq Technical Highlights – HTP System Performance * Only 1 sample required 2 sections ** RNA-Seq samples failed to generate sufficient extracted RNA to process samples. HTG works with smaller samples, is less sensitive to sample age, has a faster turnaround timeand has a higher sample success rate than RNASeq Comparison of HTG and RNA-Seq Platforms   HTG Panel RNA-Seq Number of FFPE Slides Used 1-2 * 4-8 Sample Type Used FFPE(extraction-free) FFPE(extracted RNA) Overall Pass Rate 100% (24/24) 75% (18/24) ** Pass Rate for Samples Older than 10 years 100% (13/13) 63% (7/11) Turn-Around Time 3 days 7 days Assessment of accuracy of the differential expression analysis. The Pearson correlation coefficient (Pearson Cor.) was used to assess accuracy of differential expression analysis by comparing log-fold changes of HTP FFPE lysate vs. RNA-Seq eRNA (left), HTP eRNA vs. RNA-Seq eRNA (middle) and HTP FFPE vs. HTP eRNA (right). Each data point represents a gene, and its color intensity represents the average gene expression level. 

Creating Two Categories of Potential Value: Rx/Platform Rx VALUE (Patented Novel Therapeutics) Therapeutic pipeline based on transcriptome-informed approach to drug discovery - Licensable drug candidates (near term) - HTG early development pipeline (longer term) Upfronts and milestone payments on lead candidates Milestones and royalties on approved drug sales Value depends on stage, risk, clinical indication, unmet need Platform VALUE (Pharma Partnering, CDx) Platform technology for transcriptome profiling 2nd generation pharma partnering: - HTP panel - WT miRNA panel - RNA modifications - ML designed chemical libraries - Chemi-transcriptomic profiling ‘Built in’ CDx opportunities associated with licensed drug candidate